Systems and methods for gene modification

ABSTRACT

The present disclosure provides a method for designing a set of guide RNAs for hybridizing a genomic region of interest. The present disclosure further provides methods of editing at least one genomic region of interest with at least one set of guide RNAs.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has beensubmitted electronically in ASCII format and is hereby incorporated byreference in its entirety. Said ASCII copy, created on Dec. 8, 2020, isnamed 1000_708_302_SL.txt and is 18,505 bytes in size.

BACKGROUND

Engineered nuclease technologies designed to target and manipulatespecific DNA sequences are rapidly being adopted as useful techniquesfor a number of different applications including genetic manipulation ofcells and whole organisms, targeted gene deletion, replacement andrepair, and insertion of exogenous sequences (transgenes) into thegenome. Examples of genome editing techniques include zinc finger,transcription activator-like effector (TALE), and clustered regularlyinterspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas)(“CRISPR/Cas”) systems.

The CRISPR/Cas system can be used as a gene editing tool in a plethoraof different organisms to generate breaks at a target site andsubsequently introduce mutations at the locus. Two main components canbe needed for the gene editing process: an endonuclease like Cas enzymeand a short RNA molecule to recognize a specific DNA target sequence.Instead of engineering a nuclease enzyme for every DNA target, theCRISPR/Cas system can rely on customized short RNA molecules to recruitthe Cas enzyme to a new DNA target site. Examples of Cas enzymes includeCas9 and Cpf1.

The CRISPR/Cas system can be used in prokaryotic and eukaryotic systemsfor genome editing and transcriptional regulation. In some cases, theCRISPR/Cas system can yield unwanted off-target genome editing andvaried editing efficiency across different gene targets.

SUMMARY

The present disclosure describes technologies relating to designing oneor more oligonucleotides (e.g. RNA molecules) that recognize respectivetarget oligonucleotide sequences for CRISPR/Cas mediated genemanipulation, and more specifically, the present disclosure describesmethods of determining an off-target value across the entire genome of aspecies of interest to minimize off-target genome editing and improveediting efficiency. The present disclosure describes software andhardware configurations for performing the design and validation of sucholigonucleotides.

Described herein, in certain embodiments, are methods for identifying aset of guide RNAs (gRNAs) that are hybridizable to a genomic region ofinterest in a genome comprising: designing a set of gRNAs where eachgRNA in the set of gRNAs: is hybridizable to a target site from aplurality of target sites within the genomic region of interest that isat least 30 bases apart from a different target sites in the pluralityof target sites of at least one other guide RNA from the set of guideRNAs. In some embodiments, the target site is at most 170 bases apartfrom the different target site.

In some embodiments, the sequence of at least one gRNA in the set ofgRNAs is complementary to the genomic region of interest. In someembodiments, the sequence of at least one gRNA in the set of gRNAs ispartially complementary to the genomic region of interest. In someembodiments, the sequence of the at least one gRNA in the set of gRNAspartially complementary to the genomic region of interest comprises 1,2, 3, 4, 5, or more than 5 mismatches relative to the genomic region ofinterest. In some embodiments, each gRNA in the set of gRNAs is fromabout 17 to about 42 bases in length. In some embodiments, each gRNA inthe set of gRNAs is about 20 bases in length. In some embodiments, eachgRNA in the set of gRNAs comprises a guide sequence of about 20 basesand further comprises a constant region of from about 22 to about 80bases in length. In some embodiments, the guide sequence of each gRNA inthe set of gRNAs selectively hybridizes to the genomic region ofinterest. In some embodiments, each gRNA in the initial set of gRNAs isabout 100 bases in length.

In some embodiments, the genomic region of interest comprises a codingregion of a gene. In some embodiments, the genomic region of interestcomprises an exon of the gene. In some embodiments, the genomic regionof interest comprises a family of genes. In some embodiments, thegenomic region of interest comprises one or more coding regions from thefamily of genes. In some embodiments, the genomic region of interestcomprises a non-coding region of the genome. In some embodiments, thenon-coding region is a regulatory element. In some embodiments, theregulatory element is a cis-regulatory element or a trans-regulatoryelement. In some embodiments, the cis-regulatory element is selectedfrom the group consisting of: a promoter, an enhancer, and a silencer.

In some embodiments, the genomic region of interest spans greater than 5kbs, greater than 10 kbs, greater than 15 kbs, greater than 20 kbs,greater than 50 kbs, or greater than 100 kbs. In some embodiments, theset of gRNAs comprises at least 1, at least 2, at least 3, or at least 4gRNAs. In some embodiments, at least one gRNA from the set of guide RNAscomprises a modification. In some embodiments, the modification isselected from the group consisting of: 2′-O—C1-4alkyl such as2′-O-methyl (2′-OMe), 2′-deoxy (2′-H), 2′-O—C1-3alkyl-O—C1-3alkyl suchas 2′-methoxyethyl (2′-MOE), 2′-fluoro (2′-F), 2′-amino (2′-NH2),2′-arabinosyl (2′-arabino) nucleotide, 2′-F-arabinosyl (2′-F-arabino)nucleotide, 2′-locked nucleic acid (LNA) nucleotide, 2′-unlocked nucleicacid (ULNA) nucleotide, a sugar in 1 form (1-sugar), and 4′-thioribosylnucleotide. In some embodiments, the modification is an internucleotidelinkage modification selected from the group consisting of:phosphorothioate, phosphonocarboxylate, thiophosphonocarboxylate,alkylphosphonate, and phosphorodithioate. In some embodiments, themodification is selected from the group consisting of: 2-thiouracil(2-thioU), 2-thiocytosine (2-thioC), 4-thiouracil (4-thioU),6-thioguanine (6-thioG), 2-aminoadenine (2-aminoA), 2-aminopurine,pseudouracil, hypoxanthine, 7-deazaguanine, 7-deaza-8-azaguanine,7-deazaadenine, 7-deaza-8-azaadenine, 5-methylcytosine (5-methylC),5-methyluracil (5-methylU), 5-hydroxymethylcytosine,5-hydroxymethyluracil, 5,6-dehydrouracil, 5-propynylcytosine,5-propynyluracil, 5-ethynylcytosine, 5-ethynyluracil, 5-allyluracil(5-allylU), 5-allylcytosine (5-allylC), 5-aminoallyluracil(5-aminoallylU), 5-aminoallyl-cytosine (5-aminoallylC), an abasicnucleotide, Z base, P base, Unstructured Nucleic Acid (UNA), isoguanine(isoG), isocytosine (isoC), and 5-methyl-2-pyrimidine.

In some embodiments, a target site of the plurality of target sites isadjacent to a PAM site for a nuclease selected from the group consistingof: Cas9, C2c1, C2c3, and Cpf1. In some embodiments, the nuclease isCas9. In some embodiments, the nuclease is inactivated Cas9. In someembodiments, the set of gRNAs are designed to knock-out a gene in thegenomic region of interest in a cell. In some embodiments, the cell isselected from the group consisting of: human primary cells, humanimmortalized cells, human induced pluripotent stem cells, mouseembryonic stem cells, and Chinese hamster ovary cells. In someembodiments, the designing is performed by a computer. In someembodiments, described herein, are kits comprising a set of guide RNAs(gRNAs), each gRNA in the set of gRNAs designed by any of the methodsdescribed herein.

Described herein, in certain embodiments, are kits comprising a set ofgRNAs that are hybridizable to a genomic region of interest in a genome,wherein each gRNA in the set of gRNAs: is hybridizable to a target sitefrom a plurality of target sites within the genomic region of interestthat is at least 30 bases apart from a different target site in theplurality of target sites of at least one other guide RNA from the setof guide RNAs. In some embodiments, the target site is at most 170 basesapart from the different target site. In some embodiments, the set ofgRNAs comprises at least 2, at least 3, or at least 4 gRNAs. In someembodiments, the kit further comprises one or more nucleases selectedfrom the group consisting of Cas9, C2c1, C2c3, and Cpf1. In someembodiments, the kit further comprises a plurality of sets of gRNAs,each set of gRNA hybridizable to a different genomic region of interestin the genome. In some embodiments, the one or more nucleases arecoupled to at least one gRNA.

Described herein, in certain embodiments, are methods for selecting oneor more guide RNAs (gRNAs) for hybridizing a gene of a genome of aspecies comprising: for each of a plurality of guide RNAs of an initialset of guide RNAs that hybridize to the gene, calculating an off-targetvalue by enumerating a number of mismatches to potential guide RNAhybridizing sites in the genome. In some embodiments, each gRNA in theplurality of gRNAs is 100 bases in length. In some embodiments, about 20bases of each gRNA in the plurality of gRNAs hybridizes to differenttarget site within a genomic region of interest. In some embodiments,the number of mismatches is 0. In some embodiments, the number ofmismatches is 1. In some embodiments, the number of mismatches is 2. Insome embodiments, the number of mismatches is 3. In some embodiments,the calculating enumerates an aggregate sum of the number of mismatchesfor each gRNA of the initial set of guide RNAs. In some embodiments, thecalculating organizes the number of mismatches into shards.

In some embodiments, the off-target value is calculated against areference genome. In some embodiments, the reference genome is a humanreference genome. In some embodiments, the reference genome is selectedfrom the group consisting of: Homo sapiens, Mus musculus, Cricetulusgriseus, Rattus Norvegicus, Danio rerio, and Caenorhabditis elegans. Insome embodiments, the off-target value is determined over 1,000,000 bpof a reference genome or across a reference genome. In some embodiments,the off-target value is calculated against a database of binding sitesof a nuclease. In some embodiments, the nuclease is selected from thegroup consisting of: Cas9, C2c1, C2c3, and Cpf1. In some embodiments,the nuclease is Cas9. In some embodiments, the database comprisesgreater than 10,000, greater than 50,000, greater than 100,000, greaterthan 150,000, greater than 200,000, greater than 250,000, greater than300,000, greater than 350,000, greater than 400,000, greater than450,000, greater than 500,000, greater than 550,000, greater than600,000, greater than 650,000, greater than 700,000, greater than750,000, greater than 800,000, greater than 850,000, greater than900,000, greater than 950,000, or greater than 1,000,000 binding sitesof the nuclease. In some embodiments, the database of nuclease bindingsites comprises greater than 25 million, greater than 50 million,greater than 75 million, greater than 100 million, greater than 125million, greater than 150 million, greater than 175 million, greaterthan 200 million, greater than 225 million, greater than 250 million,greater than 275 million, or greater than 300 million binding sites ofthe nuclease. In some embodiments, the calculating of the off-targetvalue by enumerating the number of mismatches is performed by acomputer.

Described herein, in certain embodiments, are methods for designing oneor more guide RNAs (gRNAs) for hybridizing to a gene of a genome of aspecies comprising: selecting a transcript from a plurality oftranscripts of the gene; and identifying an initial set of gRNAs,wherein each gRNA in the initial set of gRNAs hybridizes to differenttarget sites in the gene of the selected transcript. In someembodiments, each gRNA in the initial set of gRNAs is from about 17 toabout 42 bases in length. In some embodiments, each gRNA in the initialset of gRNAs is about 20 bases in length. In some embodiments, each gRNAin the initial set of gRNAs comprises a guide sequence of about 20 basesand a constant region of from about 22 to about 80 bases in length. Insome embodiments, the guide sequence each gRNA in the initial set ofgRNAs selectively hybridizes a target site. In some embodiments, eachgRNA in the initial set of gRNAs is about 100 bases in length. In someembodiments, the selected transcript is a most abundant transcript ofthe gene in a database. In some embodiments, the selected transcript isa longest transcript of the plurality of transcripts of the gene.

In some embodiments, the method further comprises selecting a codingregion in the gene present in the selected transcript. In someembodiments, the selected coding region is an early position exon. Insome embodiments, the early position exon is in a first half of thegene. In some embodiments, the early position exon is a first, second,third, fourth, fifth, or sixth exon of the gene. In some embodiments,the selected coding region is a selected exon that is a transcript withthe highest abundance in the plurality of transcripts of the gene. Insome embodiments, the selected exon is longer than one or more otherexons in the plurality of transcripts. In some embodiments, the selectedexon is at least 50 bp, at least 55 bp, at least 60 bp, at least 65 bp,at least 70 bp, or at least 75 bp. In some embodiments, the selectedexon is selected based on both length and abundance in the plurality oftranscripts.

In some embodiments, the method further comprises determining anoff-target value for each gRNA of the initial set of gRNAs. In someembodiments, the off-target value is determined across the genome of thespecies. In some embodiments, the genome is a reference genome of thespecies. In some embodiments, the reference genome of the species is acomplete reference assembly containing chromosomes and unlocalizedcontigs. In some embodiments, the method further comprises determiningthe off-target value by enumerating a number of mismatches for each gRNAin the initial set of gRNAs as compared to a plurality of target sitesin the genome. In some embodiments, the plurality of target sitescomprises all possible Cas nuclease binding sites across the genome. Insome embodiments, the plurality of target site comprises at least 1000,10,000, 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, 700,000,800,000, 900,000, 1,000,000, 2,000,000, or 3,000,000 target sites. Insome embodiments, the plurality of target site comprises at least100,000,000, 200,000,000, 300,000,000, 400,000,000, 500,000,000,600,000,000, 700,000,000, 800,000,000, 900,000,000, 1,000,000,000, or1,500,000,000 target sites. In some embodiments, the enumeratingcomprises determining an off-target hybridization region for each gRNAof the initial set of guide RNAs with 0, 1, 2, 3, or 4 numbers ofmismatches.

In some embodiments, a target site of the different target sites isadjacent to a PAM site for a nuclease selected from the group consistingof: Cas9, C2c1, C2c3, and Cpf1. In some embodiments, the nuclease isCas9. In some embodiments, the PAM site is NGG. In some embodiments, thenuclease is an inactivated Cas. In some embodiments, the species isselected from the group consisting of: Homo sapiens, Mus musculus,Cricetulus griseus, Rattus Norvegicus, Danio rerio, and Caenorhabditiselegans.

In some embodiments, the method further comprises selecting a subset ofguide RNAs from the initial set of gRNAs based on an on-targetefficiency threshold value and an off-target threshold value. In someembodiments, the on-target efficiency threshold value for each guide RNAof the initial set of gRNAs is determined by calculating an azimuthscore. In some embodiments, the azimuth score is greater than 0.4. Insome embodiments, the identifying is based on thresholds of the azimuthscore and the off-target hybridizing value. In some embodiments, theinitial set of gRNAs knocks-out the gene in a cell. In some embodiments,the initial set of gRNAs knocks-in a mutation into the gene in a cell.

In some embodiments, the cell is selected from the group consisting of:human primary cells, human immortalized cells, human induced pluripotentstem cells, mouse embryonic stem cells, and Chinese hamster ovary cells.In some embodiments, at least one nucleotide from at least one guide RNAin the initial set of guide RNAs comprises a modification. In someembodiments, the modification is selected from the group consisting of:2′-O—C1-4alkyl such as 2′-O-methyl (2′-OMe), 2′-deoxy (2′-H),2′-O—C1-3alkyl-O—C1-3alkyl such as 2′-methoxyethyl (2′-MOE), 2′-fluoro(2′-F), 2′-amino (2′-NH2), 2′-arabinosyl (2′-arabino) nucleotide,2′-F-arabinosyl (2′-F-arabino) nucleotide, 2′-locked nucleic acid (LNA)nucleotide, 2′-unlocked nucleic acid (ULNA) nucleotide, a sugar in 1form (1-sugar), and 4′-thioribosyl nucleotide. In some embodiments, themodification is an internucleotide linkage modification selected fromthe group consisting of: phosphorothioate, phosphonocarboxylate,thiophosphonocarboxylate, alkylphosphonate, and phosphorodithioate. Insome embodiments, the modification is selected from the group consistingof: 2-thiouracil (2-thioU), 2-thiocytosine (2-thioC), 4-thiouracil(4-thioU), 6-thioguanine (6-thioG), 2-aminoadenine (2-aminoA),2-aminopurine, pseudouracil, hypoxanthine, 7-deazaguanine,7-deaza-8-azaguanine, 7-deazaadenine, 7-deaza-8-azaadenine,5-methylcytosine (5-methylC), 5-methyluracil (5-methylU),5-hydroxymethylcytosine, 5-hydroxymethyluracil, 5,6-dehydrouracil,5-propynylcytosine, 5-propynyluracil, 5-ethynylcytosine,5-ethynyluracil, 5-allyluracil (5-allylU), 5-allylcytosine (5-allylC),5-aminoallyluracil (5-aminoallylU), 5-aminoallyl-cytosine(5-aminoallylC), an abasic nucleotide, Z base, P base, UnstructuredNucleic Acid (UNA), isoguanine (isoG), isocytosine (isoC), and5-methyl-2-pyrimidine.

In some embodiments, the selecting and the identifying are performed bya computer. In some embodiments, each gRNA in the initial set of gRNAsis hybridizable to a target site that is at least 30 bases apart fromthe target site of at least one other guide RNA from the initial set ofguide RNAs. In some embodiments, described herein, are kits comprising aset of guide RNAs (gRNAs), each gRNA in the set of gRNAs designed by anyof the methods described herein.

Described herein, in certain embodiments, are method for editing agenomic region of interest, comprising: contacting a population of cellscomprising the genomic region of interest with: (i) a set of gRNAcomprising at least two gRNAs targeting the genomic region of interestand (ii) a nuclease; wherein an editing efficiency of the set of gRNAcomprising at least two gRNAs is higher than an individual editingefficiency of each of the at least two gRNAs. In some embodiments, thegenomic region of interest is a coding region of a gene. In someembodiments, the coding region is an exon of the gene. In someembodiments, the genomic region of interest is a non-coding region in agenome. In some embodiments, the non-coding region is a regulatoryelement. In some embodiments, the regulatory element is a cis-regulatoryelement or a trans-regulatory element. In some embodiments, thecis-regulatory element is selected from the group consisting of: apromoter, an enhancer, and a silencer. In some embodiments, the methodfurther comprises contacting the cell with a donor polynucleotide. Insome embodiments, the donor polynucleotide comprises a point mutation,allele, tag or exogenous exon relative to a wild-type genotype of thecell.

In some embodiments, the editing efficiency is a proportion of cells inthe population of cells comprising a non-wild type genotype after thecontacting. In some embodiments, the non-wild type genotype is aknock-out of a gene. In some embodiments, the non-wild type genotype isan insertion or a deletion relative to a wild type genotype. In someembodiments, at least 50%, at least 60%, at least 70%, at least 80%, atleast 90%, or at least 95% of the cells in the population of cellscomprise the non-wild type genotype. In some embodiments, each gRNA ofthe at least two gRNAs hybridize to different target sites in thegenomic region of interest. In some embodiments, each gRNA of the atleast two gRNAs is hybridizable to a target site that is at least 30bases apart from the target site of at least one other guide RNA fromthe set of guide RNAs.

In some embodiments, the method further comprises introducing aplurality of sets of gRNA targeting a plurality of genomic regions ofinterest. In some embodiments, each of the plurality of sets of gRNA iscontacted with each of a plurality of subsets of the population ofcells. In some embodiments, each of the plurality of sets of gRNA targeta different genomic region of interest in the plurality of genomicregions of interest. In some embodiments, at least 50%, at least 60%, atleast 70%, at least 80%, at least 90%, or at least 95% of the cells inat least 50% of the plurality of subsets of the population of cellscomprise a non-wild type genotype. In some embodiments, at least 50%, atleast 60%, at least 70%, at least 80%, at least 90%, or at least 95% ofthe cells in at least 70% of the plurality of subsets of the populationof cells comprise a non-wild type genotype. In some embodiments, atleast 50%, at least 60%, at least 70%, at least 80%, at least 90%, or atleast 95% of the cells in at least 90% of the plurality of subsets ofthe population of cells comprise a non-wild type genotype.

In some embodiments, the method further comprises screening thepopulation of cells for a phenotype.

Described herein, in certain embodiments, are computer systems fordesigning one or more guide RNAs (gRNAs) for hybridizing to a gene of agenome of a species comprising: one or more computer processors; and anon-transient a computer readable medium comprising instructionsoperable, when executed by the one or more computer processors, to causethe system to: select a transcript from a plurality of transcripts ofthe gene, and identify an initial set of gRNAs that hybridize todifferent target sites from a plurality of target sites within in thegene of the selected transcript. In some embodiments, each gRNA in theinitial set of gRNAs is from about 17 to about 42 bases in length. Insome embodiments, each gRNA in the initial set of gRNAs is about 20bases in length. In some embodiments, each gRNA in the initial set ofgRNAs comprises a guide sequence of about 20 bases and further comprisesa constant region of from about 22 to about 80 bases in length. In someembodiments, the guide sequence of each gRNA in the initial set of gRNAsselectively hybridizes to the gene. In some embodiments, each gRNA inthe initial set of gRNAs is about 100 bases in length. In someembodiments, the selected transcript is a most abundant transcript ofthe gene in a database. In some embodiments, the selected transcript isa longest transcript of the plurality of transcripts of the gene.

In some embodiments, the instructions are further operable to cause thesystem to select a coding region in the gene present in the selectedtranscript, thereby selecting a selected coding region. In someembodiments, the selected coding region is an early position exon. Insome embodiments, the early position exon is in a first half of thegene. In some embodiments, the early position exon is a first, second,third, fourth, fifth, or sixth exon of the gene. In some embodiments,the selected coding region is a selected exon that a transcript with thehighest abundance in the plurality of transcripts of the gene. In someembodiments, the selected exon is longer than one or more other exons inthe plurality of transcripts. In some embodiments, the selected exon isat least 50 bp, at least 55 bp, at least 60 bp, at least 65 bp, at least70 bp, or at least 75 bp. In some embodiments, the selected exon isselected based on both length and abundance in the plurality oftranscripts. In some embodiments, the instructions are further operableto cause the system to determine an off-target value for each gRNA ofthe initial set of gRNAs. In some embodiments, the instructions arefurther operable to cause the system to determine across the genome ofthe species.

In some embodiments, the genome is a reference genome of the species. Insome embodiments, the reference genome of the species is a completereference assembly comprising chromosomes and unlocalized contigs. Insome embodiments, the instructions are further operable to cause thesystem to determine the off-target value by enumerating a number ofmismatches for each of the gRNAs in the initial set of gRNAs as comparedto a plurality of target sites in the genome. In some embodiments, theplurality of target sites includes all possible Cas nuclease bindingsites across the genome. In some embodiments, the plurality of targetsite comprises at least 1000, 10,000, 100,000, 200,000, 300,000,400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000,2,000,000, or 3,000,000 target sites. In some embodiments, the pluralityof target site comprises at least 100,000,000, 200,000,000, 300,000,000,400,000,000, 500,000,000, 600,000,000, 700,000,000, 800,000,000,900,000,000, 1,000,000,000, or 1,500,000,000 target sites. In someembodiments, the enumerating comprises determining an off-targethybridization region for each gRNA of the initial set of guide RNAs with0, 1, 2, 3, or 4 numbers of mismatches. In some embodiments, a targetsite of the plurality of target sites is adjacent to a PAM site for anuclease selected from the group consisting of: Cas9, C2c1, C2c3, andCpf1. In some embodiments, the nuclease is Cas9. In some embodiments,the PAM site is NGG. In some embodiments, the species is selected fromthe group consisting of: Homo sapiens, Mus musculus, Cricetulus griseus,Rattus Norvegicus, Danio rerio, and Caenorhabditis elegans.

In some embodiments, the instructions are further operable to cause thesystem to select a subset of guide RNAs from the initial set of gRNAsbased on an on-target efficiency threshold value and an off-targetthreshold value. In some embodiments, the on-target efficiency thresholdvalue for each guide RNA of the initial set of gRNAs is determined bycalculating an azimuth score. In some embodiments, the azimuth score isgreater than 0.4. In some embodiments, the instructions are furtheroperable to cause the system to identify the initial set of gRNAs basedon thresholds of the azimuth score and the off-target hybridizing value.In some embodiments, at least one nucleotide from at least one guide RNAin the initial set of guide RNAs comprises a modification. In someembodiments, the modification is selected from the group consisting of:2′-O—C1-4alkyl such as 2′-O-methyl (2′-OMe), 2′-deoxy (2′-H),2′-O—C1-3alkyl-O—C1-3alkyl such as 2′-methoxyethyl (2′-MOE), 2′-fluoro(2′-F), 2′-amino (2′-NH2), 2′-arabinosyl (2′-arabino) nucleotide,2′-F-arabinosyl (2′-F-arabino) nucleotide, 2′-locked nucleic acid (LNA)nucleotide, 2′-unlocked nucleic acid (ULNA) nucleotide, a sugar in 1form (1-sugar), and 4′-thioribosyl nucleotide. In some embodiments, themodification is an internucleotide linkage modification selected fromthe group consisting of: phosphorothioate, phosphonocarboxylate,thiophosphonocarboxylate, alkylphosphonate, and phosphorodithioate. Insome embodiments, the modification is selected from the group consistingof: 2-thiouracil (2-thioU), 2-thiocytosine (2-thioC), 4-thiouracil(4-thioU), 6-thioguanine (6-thioG), 2-aminoadenine (2-aminoA),2-aminopurine, pseudouracil, hypoxanthine, 7-deazaguanine,7-deaza-8-azaguanine, 7-deazaadenine, 7-deaza-8-azaadenine,5-methylcytosine (5-methylC), 5-methyluracil (5-methylU),5-hydroxymethylcytosine, 5-hydroxymethyluracil, 5,6-dehydrouracil,5-propynylcytosine, 5-propynyluracil, 5-ethynylcytosine,5-ethynyluracil, 5-allyluracil (5-allylU), 5-allylcytosine (5-allylC),5-aminoallyluracil (5-aminoallylU), 5-aminoallyl-cytosine(5-aminoallylC), an abasic nucleotide, Z base, P base, UnstructuredNucleic Acid (UNA), isoguanine (isoG), isocytosine (isoC), and5-methyl-2-pyrimidine. In some embodiments, each gRNA in the set: ishybridizable to a different target site within a genomic region ofinterest; and is hybridizable to a target site that is at least 30 basesapart from the target site of at least one other guide RNA from the setof guide RNAs.

Described herein, in certain embodiments, are methods for designing oneor more guide RNAs for hybridizing to a genomic region of an individualcomprising: using the individual's genome, determining gRNA target sitepotentials; for each gRNA target site potential of the gRNA target sitepotentials, determining an off-target value for a prospective guide RNA;and identifying one or more guide RNAs with an improved utility index.In some embodiments, each gRNA of the one or more gRNAs is about 100bases in length. In some embodiments, about 20 bases of each gRNA of theone or more gRNAs is hybridizable to each gRNA target site potential ofthe gRNA target site potentials. In some embodiments, the utility indexis a therapeutic index. In some embodiments, the therapeutic indexcomprises reduction of off-target binding, increased on-targetefficiency, increased knock-out efficiency, increased knock-inefficiency, or modulation of CRISPR interference. In some embodiments,the individual is a human. In some embodiments, the individual isafflicted with a condition. In some embodiments, the individual is partof a population cohort afflicted with one or more conditions. In someembodiments, the one or more conditions include one or more types ofcancer. In some embodiments, the condition is a cancer.

In some embodiments, the one or more guide RNAs are designed toknock-out a gene in the genomic region of a cell of the individual. Insome embodiments, the one or more guide RNAs are designed to knock-in amutation in the genomic region of a cell of the individual. In someembodiments, the method further comprises editing a cell with the one ormore guide RNAs with the improved utility index. In some embodiments,the determining of the gRNA target site potentials and the identifyingof the one or more guide RNAs is performed by a computer.

Described herein, in certain embodiments, are methods for assessingoff-target effect of a CRISPR agent on an individual comprising: usingthe individual's genome, determining, by a computer system, theoff-target value of the CRISPR agent by enumerating a number ofmismatches to potential target sites in the individual's genome. In someembodiments, the CRISPR agent is a therapeutic agent. In someembodiments, the CRISPR agent is a guide RNA (gRNA) that is about 100bases in length. In some embodiments, the gRNA comprises 20 bases thatare hybridizable to a target. In some embodiments, the number ofmismatches is calculated independently for each of the 20 bases that arehybridizable to a target.

In some embodiments, the enumerating comprises separately enumerating atleast two of 1, 2, 3, 4, or 5 numbers of mismatches from the potentialtarget sites. In some embodiments, the number of potential target sitesis at least 1000, 10,000, 100,000, 200,000, 300,000, 400,000, 500,000,600,000, 700,000, 800,000, 900,000, 1,000,000, 2,000,000, or 3,000,000.In some embodiments, the method further comprises outputting a reportthat enumerates the number of mismatches to potential target sites inthe individual's genome. In some embodiments, the outputting isdisplayed on a screen. In some embodiments, the assessing of theoff-target effect of the CRISPR agent is used as a companion diagnostic.

Described herein, in certain embodiments, are methods for validating aprospective gRNA comprising: determining, on a computer system, aplurality of off-target sites for the prospective gRNA in a genome orpart of the genome; calculating, using the computer system, anoff-target value for the prospective gRNA for each off-target site inthe plurality of off-target sites; and predicting, using the computersystem, activity of the prospective gRNA using the off-target value. Insome embodiments, the predicting lists a potential of on-targethybridization sites or off-target hybridization sites. In someembodiments, the genome or part of the genome is over 1,000,000 bps. Insome embodiments, the off-target value is determined by calculating anumber of mismatches for the gRNA to a plurality of off-target sites. Insome embodiments, the number of mismatches is 0, 1, 2, 3, and/or 4. Insome embodiments, the plurality of off-target sites comprises at least100,000,000 off-target sites.

Described herein, in certain embodiments, are computer systemscomprising: a user interface system configured to select of a species ofinterest and a gene of interest from the species of interest; a designmodule integrated with the user interface configured to identify one ormore guide RNA (gRNA) sequences for the gene of interest; an outputsystem configured to display selected gRNAs; and an activation unitconfigured to initiate synthesis by an RNA synthesizer of the one ormore gRNAs. In some embodiments, each gRNA is about 20 bases in length.In some embodiments, the user interface system includes a selection ofover 100, 1000, 10,000, 100,000, 500,000 different reference genomes. Insome embodiments, the design module is configured to select gRNAs basedon off-target value and on-target efficiency score. In some embodiments,the design module is configured to access to reference genomes in thecloud. In some embodiments, the design module is configured to access tomore than 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000,80,000, 90,000, 100,000, 110,000, or 120,000 reference genomes. In someembodiments, the user interface comprises: a genomic data receivingmodule for obtaining input of an individual's genome. In someembodiments, the genomic data receiving module is configured to obtainthe individual's genome from a server or from a file uploaded by a user.

Described herein, in certain embodiments, are systems comprising: aninterface configured to provide a user with access to more than 10,000reference genomes; a software configured to select one or more guideRNAs for a gene in any one of the more than 50,000 reference genomes;and an output system configured to display selected guide RNAs. In someembodiments, the system comprises more than 20,000, 30,000, 40,000,50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 110,000, or 120,000reference genomes. In some embodiments, the system further comprises ascript configured to activate and initiate synthesis of the at least oneor more guide RNAS.

Described herein, in certain embodiments, are methods for designing aguide RNA (gRNA) comprising: identifying, by a computer system, aprimary transcript of a gene; identifying, by a computer system, acommon exon between the primary transcript and a plurality ofalternative transcripts; identifying, by a computer system, a nucleasetarget site within the common exon; calculating, by a computer system, anumber of off-target binding sites in a reference genome sequence for anuclease, thereby yielding a calculated number of nuclease off-targetbinding sites; calculating, by a computer system, an on-targetefficiency score, thereby yielding a calculated on-target efficiencyscore; and outputting, by a computer system, at least one gRNA sequencewherein the at least one gRNA sequence comprises a sequence for whichthe calculated on-target efficiency is above a threshold and thecalculated number of nuclease off-target binding sites is zero. In someembodiments, the method further comprises directing a synthesis of anucleic acid that has partial complementarity to the target site. Insome embodiments, described herein, are kits comprising a set of guideRNAs (gRNAs), each gRNA in the set of gRNAs designed by any of themethods described herein.

Described herein, in certain embodiments, are systems for processing abiopolymer synthesis request from a user over a network, comprising: acommunications interface configured to communicate with a digitalcomputer of the user over the network; a reference genome databaseconfigured to store one or more reference genomes; and a computercomprising one or more computer processors operatively coupled to thecommunications interface and the database, wherein the one or morecomputer processors are individually or collectively configured to: (a)receive from the communications interface over the network, thebiopolymer synthesis request from the digital computer of the user,which biopolymer synthesis request comprises target genomic information;(b) process the target genomic information against the one or morereference genomes from the database to identify a target sequencecorresponding to the target genomic information; (c) execute analgorithm to generate a first set of guide ribonucleic acid (gRNA)sequences that are at least partially complementary to the targetsequence, and calculate an off-target complementarity score for each ofthe gRNA sequences in the first set of gRNA sequences; (d) output asecond set of gRNA sequences for display on a graphical user interfaceof the digital computer of the user, where each of the second set ofgRNA sequences has a calculated off-target complementarity score below athreshold; and (e) receive from the digital computer of the user aselection of a given gRNA sequence from the second set of gRNAsequences.

In some embodiments, the one or more computer processors areindividually or collectively programmed to direct the given gRNAsequence in a queue for synthesizing the gRNA sequence. In someembodiments, at least one genome in the reference genome database is apersonalized genome of an individual. In some embodiments, at least onegenome in the reference genome database is a set of personalized genomesof a population afflicted with a condition. In some embodiments, thereference genome is a Homo sapiens reference genome. In someembodiments, the system further comprises outputting a predicted genomicsequence, wherein the predicted genomic sequence represents a predictedoutput of editing the target genomic information with one or more gRNA'sfrom the second set of gRNA sequences. In some embodiments, thepredicted genomic sequence comprises a genomic deletion. In someembodiments, the predicted genomic sequence comprises a genomicinsertion. In some embodiments, the calculating calculates an Azimuthscore. In some embodiments, the second set of gRNA sequences displays atleast two gRNAs above a certain threshold. In some embodiments, thereference genome database comprises at least 50 thousand referencegenomes. In some embodiments, the reference genome database comprises atleast 120 thousand reference genomes.

Described herein, in certain embodiments, are methods for processing abiopolymer synthesis request from a user over a network, comprising: (a)receiving, by the computer system, the biopolymer synthesis request froma digital computer of the user over the network, which biopolymersynthesis request comprises target genomic information; (b) processing,by the computer system, the target genomic information against one ormore reference genomes from a reference genome database to identify atarget sequence corresponding to the target genomic information; (c)using one or more computer processors to execute an algorithm to (i)generate a first set of guide ribonucleic acid (gRNA) sequences that areat least partially complementary to the target sequence, and (ii)calculate an off-target complementarity score for each of the gRNAsequences in the first set of gRNA sequences for each of the gRNAsequences; (d) outputting, by the computer system, a second set of gRNAsequences for display on a graphical user interface of the digitalcomputer of the user, where each of the second set of gRNA sequencescomprises a calculated off-target complementarity score below athreshold; and (e) receiving from the digital computer of the user arequest for a synthesis of a given gRNA sequence from the second set ofgRNA sequences.

In some embodiments, the one or more computer processors receiving therequest for the synthesis are individually or collectively programmed todirect the synthesis of the given gRNA sequence from the second set ofgRNA sequences in a synthesizer. In some embodiments, at least onegenome in the reference genome database is a personalized genome of anindividual. In some embodiments, at least two genomes in the referencegenome database are personalized genomes of a population afflicted witha condition. In some embodiments, the reference genome is a Homo sapiensreference genome. In some embodiments, the method further comprisesoutputting a predicted genomic sequence, wherein the predicted genomicsequence represents a predicted output of editing the target genomicinformation with one or more gRNA's from the second set of gRNAsequences. In some embodiments, the predicted genomic sequence comprisesa genomic deletion. In some embodiments, the predicted genomic sequencecomprises a genomic insertion. In some embodiments, the calculatingcalculates an Azimuth score. In some embodiments, the second set of gRNAsequences displays at least two gRNAs above a certain threshold. In someembodiments, the reference genome database comprises at least 50thousand reference genomes. In some embodiments, the reference genomedatabase comprises at least 120 thousand reference genomes.

In some embodiments, described herein, are non-transitory computerreadable mediums comprising instructions operable, when executed by oneor more computer processors, to cause the one or more computerprocessors to perform any of the methods described herein.

Described herein, in certain embodiments, are non-transitorycomputer-readable mediums comprising machine executable code that, uponexecution by one or more computer processors, implements a method forprocessing a biopolymer synthesis request from a user over a network,the method comprising: (a) receiving the biopolymer synthesis requestfrom a digital computer of the user over the network, which biopolymersynthesis request comprises target genomic information; (b) processingthe target genomic information against one or more reference genomesfrom a reference genome database to identify a target sequencecorresponding to the target genomic information; (c) executing analgorithm to generate a first set of guide ribonucleic acid (gRNA)sequences that are at least partially complementary to the targetsequence, and calculate an off-target complementarity score for each ofthe gRNA sequences in the first set of gRNA sequences; (d) outputting asecond set of gRNA sequences for display on a graphical user interfaceof the digital computer of the user, where each of the second set ofgRNA sequences has a calculated off-target complementarity score below athreshold; and (e) receiving from the digital computer of the user aselection of a given gRNA sequence from the second set of gRNAsequences.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.To the extent publications and patents or patent applicationsincorporated by reference contradict the disclosure contained in thespecification, the specification is intended to supersede and/or takeprecedence over any such contradictory material.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings (also “Figure” and “FIG.” herein), of which:

FIG. 1 shows an example of a flowchart of a method of designing one ormore guides for hybridizing to a gene of a genome of a species.

FIG. 2 shows an example of a table of a plurality of transcripts of agene of a genome of a species.

FIG. 3 shows an example of an early coding region of a transcript to betargeted by one or more gRNAs.

FIG. 4A shows examples of plots of relative representation and exonlength of a plurality of exons from a transcript. FIG. 4B shows examplesof guides and their off-target and on-target activity analyses. Figurediscloses SEQ ID NOS 1-8, respectively, in order of appearance.

FIG. 5 shows a data processing architecture for calculating off-targetvalues of a plurality of guides across a genome. Figure discloses SEQ IDNOS 9, 9-21, 21-38, 8, 39, 6, 40, 2, 41-44, 44-45, respectively, inorder of appearance.

FIG. 6 shows an example of a flowchart of a method of validating one ormore guides for hybridizing to a gene of a genome of a species.

FIGS. 7A-7D illustrate examples of a window of a graphical userinterface (GUI) for selecting a genome and a gene of interest to requestdesigning of one or more guides for hybridizing the gene of the genome.FIG. 7A illustrate a window prior to selecting a genome of interest anda gene of interest. FIG. 7B illustrates a window showing a list ofgenomes that match a typed input.

FIG. 7C illustrates a window showing a list of genes that match a typedinput. FIG. 7D illustrates a window after selection of a genome ofinterest, a gene of interest, and a nuclease.

FIG. 8 illustrates an example of a window of the GUI for displaying aprogress of designing one or more guides for hybridizing the gene of thegenome of interest.

FIGS. 9A-9D illustrate examples of a window of the GUI for displayingone or more guides that are designed to hybridize the gene of the genomeof interest. FIG. 9A illustrates a window showing a summary of thedesigning the one or more gRNAs. FIG. 9A discloses SEQ ID NOS 46-49,respectively, in order of appearance. FIG. 9B illustrates selection ofthe top ranked gRNA. FIG. 9B discloses SEQ ID NOS 46-49, respectively,in order of appearance. FIG. 9C illustrates a window showing informationabout the selected gRNA. Figure discloses the Target sequence as SEQ IDNO: 4. FIG. 9C also discloses gRNA as SEQ ID NOS 46-49, respectively, inorder of appearance. FIG. 9D illustrates a window showing additionalgRNAs designed to hybridize to the gene of the genome of interest. FIG.9D discloses SEQ ID NOS 50-59, respectively, in order of appearance.

FIGS. 10A-10E illustrate examples of a window of the GUI for displayingdetailed information about a designed guide. FIG. 10A illustrates asummary of the performance of the selected gRNA. FIG. 10A discloses SEQID NOS 3 and 60-64, respectively, in order of appearance. FIG. 10Billustrates a schematic of the Cas-gRNA complex interacting with thetarget region of interest, with the RNA guide sequence selected. FIG.10B discloses SEQ ID NOS 60-62, respectively, in order of appearance.FIG. 10C illustrates a schematic of the Cas-gRNA complex interactingwith the target region of interest, with the PAM region selected. FIG.10C discloses SEQ ID NOS 60-62, respectively, in order of appearance.FIG. 10D illustrates a schematic of the Cas-gRNA complex interactingwith the target region of interest, with the target sequence selected.FIG. 10D discloses SEQ ID NOS 60-62, respectively, in order ofappearance. FIG. 10E illustrates a list of off-target sites of theselected gRNA. FIG. 10E discloses SEQ ID NOS 63-82, respectively, inorder of appearance.

FIGS. 11A-11B illustrate examples of a window of the GUI for selectingand purchasing a subset of the one or more guides that are designed tohybridize the gene of the genome of interest. FIG. 11A illustrates awindow showing selection of a subset of gRNAs. FIG. 11A discloses SEQ IDNOS 3 and 47-49, respectively, in order of appearance. FIG. 11Billustrates a window showing the selected gRNAs with an additionalchoice to order modified or unmodified gRNAs. FIG. 11B discloses SEQ IDNOS 47 and 3, respectively, in order of appearance.

FIGS. 12A-12B illustrate examples of a window the GUI for selecting agenome of a species of interest and inputting a previously generatedguide sequence to request validation of the guide performance. FIG. 12Aillustrates a window prior to selecting a genome of interest and a guidesequence. FIG. 12B illustrates a window after selection of a genome ofinterest and a guide sequence (SEQ ID NO: 3).

FIGS. 13A-13B illustrate examples of a window of the GUI for displayingdetailed information about validation of a guide. FIG. 13A illustrates asummary of the performance of the predetermined gRNA. FIG. 13A disclosesSEQ ID NOS 3 and 60-62, respectively, in order of appearance. FIG. 13Billustrates a list of off-target sites of the predetermined gRNA. FIG.13B discloses SEQ ID NOS 65, 63, 64, 83, 80, 80, 77, 66, 84, 85, 69-71,73, 80 and 86-90, respectively, in order of appearance.

FIG. 14 shows a computer system that can be programmed or otherwiseconfigured to implement methods provided herein.

FIG. 15 illustrates editing efficiency of single guide RNA vs. multipleguide RNA. For single guide RNA, each data point represents the percentediting efficiency, or KO Score, of one transfected sgRNA. Formulti-guide, each data point represents the KO Score for threeco-transfected sgRNAs.

FIG. 16 illustrates percent editing efficiency relative to spacing ofguide RNAs in a multi-gRNA set.

FIG. 17 illustrates percent editing efficiency of double knockouts usingmultiple gRNAs for each pair of genes targeted.

FIGS. 18A-18B illustrate screening of arrayed libraries using amulti-guide knock-out design. FIG. 18A illustrates screening of alibrary for a functional measure. FIG. 18B illustrates screening of alibrary for editing efficiency.

DETAILED DESCRIPTION

While various embodiments of the invention have been shown and describedherein, it will be obvious to those skilled in the art that suchembodiments are provided by way of example only. Numerous variations,changes, and substitutions can occur to those skilled in the art withoutdeparting from the invention. It should be understood that variousalternatives to the embodiments of the invention described herein can beemployed.

The terminology used herein is for the purpose of describing particularcases only and is not intended to be limiting. The below terms arediscussed to illustrate meanings of the terms as used in thisspecification, in addition to the understanding of these terms by thoseof skill in the art. As used herein and in the appended claims, thesingular forms “a,” “an,” and, “the” include plural referents unless thecontext clearly dictates otherwise. It is further noted that the claimscan be drafted to exclude any optional element. As such, this statementis intended to serve as antecedent basis for use of such exclusiveterminology as “solely,” “only,” and the like in connection with therecitation of claim elements, or use of a “negative” limitation.

Certain ranges are presented herein with numerical values being precededby the term “about.” The term “about” is used herein to provide literalsupport for the exact number that it precedes, as well as a number thatis near to or approximately the number that the term precedes. Indetermining whether a number is near to or approximately a specificallyrecited number, the near or approximating un-recited number may be anumber which, in the context in which it is presented, provides thesubstantial equivalent of the specifically recited number. Where a rangeof values is provided, it is understood that each intervening value, tothe tenth of the unit of the lower limit unless the context clearlydictates otherwise, between the upper and lower limit of that range andany other stated or intervening value in that stated range, isencompassed within the methods and compositions described herein. Theupper and lower limits of these smaller ranges may independently beincluded in the smaller ranges and are also encompassed within themethods and compositions described herein, subject to any specificallyexcluded limit in the stated range. Where the stated range includes oneor both of the limits, ranges excluding either or both of those includedlimits are also included in the methods and compositions describedherein.

The term “polynucleotide” or “nucleic acid,” as used interchangeablyherein, can generally refer to a polymeric form of nucleotides of anylength, either ribonucleotides and/or deoxyribonucleotides. Thus, theseterm include, but are not limited to, single-, double-, ormulti-stranded DNA or RNA, genomic DNA, complementary DNA (cDNA), guideRNA (gRNA), messenger RNA (mRNA), DNA-RNA hybrids, or a polymercomprising purine and pyrimidine bases or other natural, chemically orbiochemically modified, non-natural, or derivatized nucleotide bases.The term “oligonucleotide,” as used herein, can generally refer to apolynucleotide of between about 5 and about 100 nucleotides of single-or double-stranded DNA or RNA. However, for the purposes of thisdisclosure, there may be no upper limit to the length of anoligonucleotide. In some cases, oligonucleotides can be known as“oligomers” or “oligos” and can be isolated from genes, or chemicallysynthesized by methods known in the art. The terms “polynucleotide” and“nucleic acid” should be understood to include single-stranded (such assense or antisense) and double-stranded polynucleotides.

The term “modified nucleotide,” as used herein, can generally refer to anucleotide having a modification to the chemical structure of one ormore of the base, the sugar, and/or the phosphodiester linkage orbackbone portions, including the nucleotide phosphates, relative to anaturally occurring base, sugar, and/or phosphodiester linkage orbackbone portions.

The term “hybridization” or “hybridizing,” as used herein, can generallyrefer to a process where completely or partially complementarypolynucleotide strands come together under suitable hybridizationconditions to form a double-stranded structure or region in which thetwo constituent strands are joined by hydrogen bonds. In some cases,modified nucleotides can form hydrogen bonds that allow or promotehybridization. In some cases, a guanine (G) of a protein-binding segmentof a subject DNA-targeting RNA molecule can be considered complementaryto a uracil (U), and vice versa.

The term “cleavage” or “cleaving,” as used herein, can generally referto breaking of the covalent phosphodiester linkage in the ribosylphosphodiester backbone of a polynucleotide. The term “cleavage” or“cleaving” can encompass cleavage that results in both single-strandedbreaks and double-stranded breaks. In some cases, a cleavage may resultin the production of either blunt ends or staggered (or sticky) ends.

The term “CRISPR/Cas,” as used herein, can refers to a ribonucleoproteincomplex comprising a guide RNA (gRNA) and a CRISPR-associated (Cas)endonuclease. The term “CRISPR” refers to the Clustered RegularlyInterspaced Short Palindromic Repeats and the related system thereof.While CRISPR was discovered as an adaptive defense system that enablesbacteria and archaea to detect and silence foreign nucleic acids (e.g.,from viruses or plasmids), it can be adapted for use in a variety ofcell types to allow for polynucleotide editing in a sequence-specificmanner. In some cases, one or more elements of a CRISPR system can bederived from a type I, type II, or type III CRISPR system. In the CRISPRtype II system, the guide RNA can interact with Cas and direct thenuclease activity of the Cas enzyme to a target region. The targetregion can comprise a “protospacer” and a “protospacer adjacent motif”(PAM), and both domains can be needed for a Cas enzyme mediated activity(e.g., cleavage). The protospacer can be referred to as a target site(or a genomic target site). The gRNA can pair with (or hybridize) theopposite strand of the protospacer (binding site) to direct the Casenzyme to the target region. The PAM site generally refers to a shortsequence recognized by the Cas enzyme and, in some cases, required forthe Cas enzyme activity. The sequence and number of nucleotides for thePAM site can differ depending on the type of the Cas enzyme.

The term “Cas,” as used herein, can generally refer to a wild type Casprotein, a fragment thereof, or a mutant or variant thereof.

A Cas protein can comprise a protein of or derived from a CRISPR/Castype I, type II, or type III system, which can be an RNA-guidedpolynucleotide-binding or nuclease activity. Examples of suitable Casproteins include CasX, Cas3, Cas4, Cas5, Cas5e (or CasD), Cash, Cas6e,Cas6f, Cas7, Cas8a1, Cas8a2, Cas8b, Cas8c, Cas9 (also known as Csn1 andCsx12), Cas10, Cas10d, CasF, CasG, CasH, Csy1, Csy2, Csy3, Cse1 (orCasA), Cse2 (or CasB), Cse3 (or CasE), Cse4 (or CasC), Csc1, Csc2, Csa5,Csn2, Csm2, Csm3, Csm4, Csm5, Csm6, Cmr1, Cmr3, Cmr4, Cmr5, Cmr6, Csb1,Csb2, Csb3, Csx17, Csx14, Csx10, Csx16, CsaX, Csx3, Csz1, Csx15, Csf1,Csf2, Csf3, Csf4, Cu1966, homologues thereof, and modified versionsthereof. In some cases, a Cas protein can comprise a protein of orderived from a CRISPR/Cas type V or type VI system, such as Cpf1(Cas12a), C2c1 (Cas12b), C2c2, homologues thereof, and modified versionsthereof. In some cases, a Cas protein can be a catalytically dead orinactive Cas (dCas).

In some cases, the Cas protein can be a Cas9 protein. In some cases, thePAM sequence recognized by the Cas9 protein can be NGG, where “N” is anynucleotide.

The term “guide RNA” or “gRNA,” as used herein, can generally refer toan RNA molecule (or a group of RNA molecules collectively) that can bindto a Cas protein and aid in targeting the Cas protein to a specificlocation within a target polynucleotide (e.g., a DNA). A guide RNA cancomprise a CRISPR RNA (crRNA) segment and a trans-activating crRNA(tracrRNA) segment. The term “crRNA” or “crRNA segment,” as used herein,can refer to an RNA molecule or portion thereof that includes apolynucleotide-targeting guide sequence, a stem sequence, and,optionally, a 5′-overhang sequence. The term “tracrRNA” or “tracrRNAsegment,” can refer to an RNA molecule or portion thereof that includesa protein-binding segment (e.g., the protein-binding segment is capableof interacting with a CRISPR-associated protein, such as a Cas9). Theterm “guide RNA” can encompasses a single guide RNA (sgRNA), where thecrRNA segment and the tracrRNA segment are located in the same RNAmolecule. The term “guide RNA” can also encompasses, collectively, agroup of two or more RNA molecules, where the crRNA segment and thetracrRNA segment are located in separate RNA molecules.

In some cases, the CRISPR/Cas activity can be useful in any in vitro orin vivo application in which it is desirable to modify a nucleic acid ina site-specific (targeted) way, for example gene knock-out (KO), geneknock-in (KI), gene editing, gene tagging, etc., as used in, forexample, gene therapy. The nucleic acid can be DNA or RNA. Examples ofgene therapy include treating a disease or as an antiviral,antipathogenic, or anticancer therapeutic; the production of geneticallymodified organisms in agriculture; the large scale production ofproteins by cells for therapeutic, diagnostic, or research purposes; theinduction of induced pluripotent stem cells (iPS cells or iPSCs); andthe targeting of genes of pathogens for deletion or replacement. In somecases, the Cas can be a catalytically dead or inactive Cas (dCas), andthe resulting CRISPR/dCas system can be useful for sequence-specificrepression (CRISPR interference) or activation (CRISPR activation) ofgene expression.

The term “subject,” “individual,” or “patient,” as used herein, cangenerally refer to whole organism or a collection thereof that can be inneed of and/or subjected to a treatment, such as a farm animal,companion animal, or human, or a collection thereof. In some cases, theterm “subject” can be a cell or a cell line thereof.

The term “gene,” as used herein, can generally refer to a nucleotidesequence that encodes functional genetic information, such as forexample, a nucleotide sequence encoding a polypeptide (e.g., protein), atransfer RNA (tRNA), or a ribosomal RNA (rRNA). The gene can compriseDNA, RNA, or other nucleotides.

Methods for Designing Oligonucleotides

In an aspect, the present disclosure provides a method for designing oneor more guide RNAs (gRNAs) for hybridizing to a genomic region ofinterest. The genomic region of interest can be a gene of a genome of aspecies. The method can comprise selecting a transcript from a pluralityof transcripts of the gene. The method can comprise identifying aninitial set of gRNAs that hybridize different target sites in the geneof the selected transcript. The gene can be a gene of interest. Thegenomic region of interest can be a non-coding region of the genome. Thenon-coding region can be a regulatory element. The regulatory elementcan be a cis-regulatory element or a trans-regulatory element. Thecis-regulatory element can be a promoter, an enhancer, or a silencer.

Information comprising the genome of the species and/or a referencegenome of the species can be obtained from a plurality of databases. Insome cases, the plurality of databases can include gene and/or genomedatabases comprising sequencing data from DNA (DNA-seq) and/or RNA(RNA-seq). Examples of such genome databases include GENCODE, NCBI,Ensembl, {APPRIS}, and NIH Human Microbiome Project. Alternatively or inaddition to, genomic information of an individual can be retrieved frompersonalized genome databases, including, but are not limited to,23andMe, deCODE Genetics, Gene by Gene, Gene Planet, DNA Ancestry,uBiome, and healthcare providers. In some cases, necessary informationcomprising at least a portion of the genome of the species of interestcan be provided by a user (e.g., via a user interface on a user devicesuch as a personal computer).

The genome of the species can comprise some or a complete set of geneticmaterial present in the species (e.g., cell or organism). Examples ofthe species include, but are not limited to, mammals (e.g., Homosapiens, Mus musculus, Cricetulus griseus, Rattus norvegecus, Panpaniscus), fish (e.g., Danio rerio, Amphiprion frenatus), insect (e.g.,Drosophila melanogaster), plants (e.g., Arabidopsis thaliana),roundworms (e.g., Caenorhabditis elegans), and microorganisms includingbacteria (e.g., Escherichia coli, Lactobacillus bulgaricus). In somecases, the bacteria can include strains that are to be consumed by anindividual as a supplement (e.g., in yogurt as a medium) and/or as atreatment (e.g., to suppress or ameliorate a condition). In some cases,the bacteria can include strains that are present in the body of anindividual (e.g., human microbiome).

The genetic material of the genome can be DNA and/or RNA. The geneticmaterial can include nucleic acid sequences in genes and intergeneticregions. In some cases, the genetic material can be represented as aunit of a chromosome. In some cases, the genetic material can berepresented as one or more transcripts that have been transcribed from agene. The gene and its respective one or more transcripts can compriseone or more coding regions (i.e., exons). In some cases, the gene andits respective one or more transcripts can comprise one or moreintragenic non-coding regions (i.e., introns). The one or moreintragenic non-coding regions can be located between the coding regions.In some cases, a gene can encode one transcript. In some cases, a geneencode a plurality of transcripts, each transcript comprising differentvariations of exons and introns from the gene. In an example, the RelAgene encodes for transcription factor p65, and the RELA gene of Homosapiens encodes at least 18 known transcripts of varied length.RELA-202, RELA-207, RELA-226, RELA-205, RELA-201, RELA-208, RELA-220,RELA-207, RELA-215, RELA-204, RELA-222, RELA-213, RELA-225, RELA-211,RELA-219, RELA-221, and RELA-212. Thus, the plurality of transcripts canhave different numbers of nucleotide bases (polynucleotide lengths).Alternatively or in addition to, the plurality of transcripts can betranslated into polypeptides (e.g., proteins) having different numbersof amino acids (polypeptide lengths). In some cases, each of theplurality of transcripts can have different expression levels(abundance) reported relative to one or more other transcripts.

To identify the initial set of gRNAs for hybridizing the gene, atranscript can be selected from the plurality of transcripts of thegene. In some cases, the selected transcript can have a higher abundancereported than one or more other transcripts in the plurality oftranscripts. In some embodiments, the abundance of the plurality oftranscripts of the gene is determined from a database. The selectedtranscript can have the first, second, third, fourth, or fifth highestabundance reported in the plurality of transcripts. In some cases, theselected transcript can have at least one additional nucleotide than oneor more other transcripts in the plurality of transcripts. The selectedtranscript can have the first, second, third, fourth, of fifth largestnumber of nucleotides in the plurality of transcripts. In some cases, atranslated polypeptide (e.g., protein) from the selected transcript canhave at least one additional amino acid than one or more polypeptidestranslated from one or more other transcripts in the plurality oftranscripts. The translated polypeptide from the selected transcript canhave the first, second, third, fourth, or fifth largest number of aminoacids in the plurality of transcripts. In some cases, the abundance ofthe plurality of transcripts can be a first criterion used to determinethe selected transcript from the plurality of transcripts of the gene.

To identify the initial set of gRNAs for hybridizing to the gene, acoding region in the gene present in the selected transcript can beselected. If the gene is DNA, the selected coding region can be closerto a promoter (upstream) of the gene than a terminator (downstream) ofthe gene. If the gene is RNA, the selected coding region can be closerto a 5′ end of the gene than a 3′ end of the gene. In some cases, theselected coding region can be an early position exon within the selectedtranscript. The early position exon can be an exon that is locatedwithin the first half of the gene. The early position exon can be thefirst, second, third, fourth, fifth, of sixth exon of the gene.

In some cases, the selected coding region of the selected transcript canbe an exon that has a higher prevalence than one or more other exonspresent in the one or more of the plurality of transcripts of the gene.In some cases, the selected exon of the selected transcript can becontained (common) in about 50% of the other transcripts in theplurality of transcripts. The selected exon of the selected transcriptcan be contained in at least about 40 percent (%), 45%, 50%, 55%, 60%,65%, 70%, 75%, 80%, 85%, 90%, 95%, or more of the other transcripts inthe plurality of transcripts. The selected exon of the selectedtranscript can be contained in at most about 95%, 90%, 85%, 80%, 75%,70%, 65%, 60%, 55%, 50%, 45%, 40%, or less of the other transcripts inthe plurality of transcripts. In some cases, the selected exon of theselected transcript can have at least one additional nucleotide than theother exons in the selected transcript. In some cases, the selected exoncan have at least 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100,105, 110, 115, 120, or more nucleotides. In some cases, both theprevalence and the number of nucleotides of the exons can be thecriteria to determine the selected exon of the selected transcript.

The initial set of gRNAs can be designed to hybridize to a targetregion, also referred to herein as a binding site. The target region canbe in a gene or a portion of the gene in the genome of the species. Insome cases, the portion of the gene can be an exon of the gene. The exoncan be an exon found in each transcript of the gene. The exon can be theselected exon of a selected transcript of the plurality of transcriptsof the gene from the aforementioned criteria. In some cases, one or moregRNAs in the initial set of gRNAs can be a single guide RNA (sgRNA). Insome cases, the sgRNA can be a single polynucleotide chain. The sgRNAcan comprise a hybridizing polynucleotide sequence and a secondpolynucleotide sequence.

The hybridizing polynucleotide sequence can hybridize to the portion ofthe gene (e.g., the selected exon of the selected transcript of theplurality of transcripts of the gene). The hybridizing polynucleotidesequence of the sgRNA can range between 17 to 23 nucleotides. Thehybridizing polynucleotide sequence of the sgRNA can be at least 17, 18,19, 20, 21, 22, 23, or more nucleotides. The hybridizing polynucleotidesequence of the sgRNA can be at most 23, 22, 21, 20, 19, 18, 17, or lessnucleotides. In an example, the hybridizing polynucleotide sequence ofthe gRNA is 20 nucleotides. The hybridizing polynucleotide sequence canbe complementary or partially complementary to the target region. Ahybridizing polynucleotide sequence complementary to the target regioncan comprise a sequence with 100% complementarity to a sequence of thetarget region. A gRNA partially complementary to the target region cancomprise a sequence with at least 1, at least 2, at least 3, at least 4,or at least 5 mismatches relative to a sequence comprising 100%complementary to the target region.

The second polynucleotide sequence of the single polynucleotide chainsgRNA can interact (bind) with the Cas enzyme. The second polynucleotidesequence can be about 80 nucleotides. The second polynucleotide sequencecan be 80 nucleotides. The second polynucleotide sequence can be atleast 80, or more nucleotides. The second polynucleotide sequence can beat most 80, or less nucleotides.

Overall, the single polynucleotide chain sgRNA can range between 97 to103 nucleotides. The single polynucleotide chain sgRNA can be at least97, 98, 99, 100, 101, 102, 103, or more nucleotides. The singlepolynucleotide chain sgRNA can be at most 103, 102, 101, 100, 99, 98,97, or less nucleotides. In an example, the single polynucleotide chainsgRNA can be 100 nucleotides.

In some cases, one or more gRNAs in the initial set of gRNAs can be acomplex (e.g., via hydrogen bonds) of a CRISPR RNA (crRNA) segment and atrans-activating crRNA (tracrRNA) segment. The crRNA can comprise ahybridizing polynucleotide sequence and a tracrRNA-bindingpolynucleotide sequence. The hybridizing polynucleotide sequence canhybridize to the portion of the gene (e.g., the selected exon of theselected transcript of the plurality of transcripts of the gene). Thehybridizing polynucleotide sequence of the crRNA can range from 17 to 23nucleotides. The hybridizing polynucleotide sequence of the crRNA can beat least 17, 18, 19, 20, 21, 22, 23, or more nucleotides. Thehybridizing polynucleotide sequence of the crRNA can be at most 23, 22,21, 20, 19, 18, 17, or less nucleotides. In an example, the hybridizingpolynucleotide sequence of the crRNA is 20 nucleotides. ThetracrRNA-binding polynucleotide sequence of the crRNA can be 22nucleotides. The tracrRNA-binding polynucleotide sequence of the crRNAcan be at least 22, or more nucleotides. The tracrRNA-bindingpolynucleotide sequence of the crRNA can be at most 22, or lessnucleotides. Overall, the crRNA can range from 39 to 45 nucleotides. ThecrRNA can be at least 39, 40, 41, 42, 43, 44, 45, or more nucleotides.The crRNA can be at most 45, 44, 43, 42, 41, 40, 39, or lessnucleotides. The tracrRNA can range from 60 and 80 nucleotides. ThetracrRNA can be at least 60, 61, 62, 63, 64, 66, 68, 70, 72, 74, 76, 78,80, or more nucleotides. The tracrRNA can be at most 80, 79, 78, 77, 76,74, 72, 70, 68, 66, 64, 62, 60, or less nucleotides. In an example, thetracrRNA can be 72 nucleotides. In another example, the hybridizingpolynucleotide sequence of the crRNA is 20 nucleotides, the crRNA is 42nucleotides, and the respective tracrRNA is 72 nucleotides.

In some cases, the initial set of gRNAs can comprise both one or moresgRNAs and one or more complexes of the crRNA and the tracrRNA.Alternatively or in addition to, one or more gRNAs in the initial set ofgRNAs can be a complex of three or more RNA chains. At least one RNAchain of the complex of three or more RNA chains can comprise ahybridizing polynucleotide sequence. At least one RNA chain of thecomplex of three or more RNA chains can comprise a Cas enzyme bindingsequence.

When the gRNA hybridizes to the target region of the genomic region ofinterest, the hybridized portion of the genomic region of interest canbe a target region (or target locus) that comprises a protospacer(target site), a protospacer adjacent motif (PAM) that is recognized bythe Cas enzyme, and the opposite strand of the protospacer (bindingsite). The opposite strand of the protospacer can be thegRNA-hybridizing genomic region (sequence). The gRNA-hybridizingsequence in the gene can range from 17 to 23 nucleotides. ThegRNA-hybridizing sequence in the gene can be at least 17, 18, 19, 20,21, 22, 23, or more nucleotides. The gRNA-hybridizing sequence in thegene can be at most 23, 22, 21, 20, 19, 18, 17, or less nucleotides.

Each of the gRNAs in the initial set of gRNAs can be designed to bindits respective binding site in the genomic region of interest (e.g., abinding site in the selected exon). However, in some cases, each of thegRNAs can also bind other Cas target regions that comprise the PAM site,resulting in an undesirable, off-target binding to an off-targethybridization region. As such, for each of the gRNA of the initial setof gRNAs, an off-target value can be determined. The off-target valuecan be determined across the genome of the species. In some cases, theoff-target value can be determined across a reference genome (e.g., ahuman reference genome, a microbiome genome, etc.) of the species. Thereference genome of the species can be a set of genes assembled fromsequencing of DNA (or RNA) from a collection of donors. The referencegenome can comprise genetic material from one or more chromosomes. Thereference genome can comprise one or more contigs (e.g., unlocalizedsequence contigs). Each contig can be a set of overlappingpolynucleotide clones that represent a continuous region of DNA. In anexample, each contig can be a continuous DNA sequence. The off-targetvalue can be determined (e.g., calculated) by enumerating a number ofmismatches for each of the gRNAs in the initial set of gRNAs as comparedto a plurality of target sites in the genome. The plurality of targetsites can include protospacers of all possible Cas nuclease targetregions across the genome.

In some cases, each of the plurality of target sites can be adjacent toa PAM site. In some cases, each of the plurality of target sites can beadjacent to a PAM site for a nuclease selected from the group consistingof: Cas9, C2c1, C2c3, Cpf1, Cas13b, and Cas13c. In an example, the Casnuclease is Cas9 from Streptococcus pyogenes (SpCas9), and the pluralityof target sites include all nucleotide sequences adjacent to the PAMsite of SpCas9 (NGG, where “N” is any nucleotide). In another example,the Cas nuclease is Cas9 from Neisseria meningitidis (NmCas9), and theplurality of target sites include al nucleotide sequences adjacent tothe PAM site of NmCas9 (GATT). To be directed to such target sites, oneor more of the nucleases (e.g., Cas9, C2c1, C2c3, Cpf1, Cas13b, Cas13c,etc.) can be coupled to at least one gRNA. The at least one gRNA can bedesigned to hybridize at least one binding site that is an oppositestrand of the target site (protospacer).

In an example, a plurality of target sites reported for bacterium caninclude at least 100, 1,000, 10,000, 20,000, 30,000, 40,000, 50,000,60,000, 70,000, 80,000, 90,000, 100,000, or more target sites. Inanother example, a plurality of target sites reported for human caninclude at least 1000, 10,000, 100,000, 1,000,000, 10,000,000,20,000,000, 30,000,000, 40,000,000, 50,000,000, 60,000,000, 70,000,000,80,000,000, 90,000,000, 100,000,000, 200,000,000, 300,000,000, or moretarget sites. In another example, a plurality of target sites reportedfor plants can include at least 10,000, 100,000, 1,000,000, 10,000,000,100,000,000, 200,000,000, 300,000,000, 400,000,000, 500,000,000,600,000,000, 700,000,000, 800,000,000, 900,000,000, 1,000,000,000,1,100,000,000, 1,200,000,000, 1,300,000,000, 1,400,000,000,1,500,000,000, or more target sites.

In some cases, enumerating the number of mismatches for each of thegRNAs as compared to the plurality of target sites can comprisedetermining the off-target hybridizing region with 0, 1, 2, 3, 4, 5, ormore numbers of mismatches. This can be determined across an entiregenome for which the gRNA is designed for or a portion of such a genome.The genome can be a reference genome. In some cases, the portion of thegenome can be at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or morechromosomes. The number of mismatches can be calculated independentlyfor each of the gRNA (e.g., a gRNA comprising a hybridizingpolynucleotide sequence of 20 bases) that are hybridizable to thetarget. The number of enumerated mismatches can be 0. The number ofenumerated mismatches can be 1. The number of enumerated mismatches canbe 2. The number of enumerated mismatches can be 3. The number ofenumerated mismatches can be 4. In some cases, determining (e.g.,calculating) the off-target value comprises enumerating an aggregate sumof the number of mismatches for each of the initial set of gRNAs. Insome cases, the enumerating can comprise separately enumerating at leasttwo of 0, 1, 2, 3, 4, or 5 numbers of mismatches from the target sites(potential target sites). In an example, for a designed gRNA, there canbe 1 off-target hybridizing region with 0 mismatch (e.g., verynucleotide of the gRNA is collectively paired with a respectivenucleotide of the off-target hybridizing region), 3 off-targethybridizing region with 1 mismatch, 5 off-target hybridizing region with2 mismatches, 7 off-target hybridizing region with 3 mismatches, and 9off-target hybridizing region with 4 mismatches. Thus, the resultingoff-target value of the designed gRNA can be denoted as [1, 3, 5, 7, 9].In another example, for another designed gRNA, there can be 0 off-targethybridizing region with 0 mismatch (e.g., every nucleotide of the gRNAis collectively paired with a respective nucleotide of the off-targethybridizing region), 0 off-target hybridizing region with 1 mismatch, 15off-target hybridizing region with 2 mismatches, 50 off-targethybridizing region with 3 mismatches, and 90 off-target hybridizingregion with 4 mismatches. Thus, the resulting off-target value of thedesigned gRNA can be denoted as [0, 0, 15, 50, 90].

The off-target value can be used as a criterion to select a subset ofgRNAs from the initial set of gRNAs. In some cases, one of the numbersof mismatches can be used as a threshold to generate the subset of gRNAsfrom the initial set of gRNAs. In an example, the subset of gRNAs cannot have any off-target hybridization region with 0 mismatch. In suchcase, each of the subset of gRNAs can have an off-target value of [0, #,#, #, #], where “#” denotes any integer of at least 0. In anotherexample, the subset of gRNAs can not have any off-target hybridizationregion with 0 and 1 mismatch. In such case, each of the subset of gRNAscan have an off-target value of [0, 0, #, #, #], where “#” denotes anyinteger of at least 0. Not wishing to be bound by theory, increasing thethreshold can yield a subset of gRNAs with lower chance of off-targeteffects in vitro or in vivo.

An on-target efficiency value for each gRNA of the initial set of gRNAscan be determined. The off-target efficiency value of the gRNA can bedetermined by calculating an azimuth score. The azimuth score can bebased on the Doench “Rule Set 2” scoring model. The Rule Set 2 scoringmodel can use one or more machine learning algorithms to calculate theon-target activity of each gRNA to its respective target region.Examples of parameters used by the one or more machine learningalgorithms include: the position of single nucleotides; the position ofdinucleotides; the frequency of the single and di-nucleotides; thenumber of G and C bases in the gRNA; the location of the gRNA within thegene; and the melting temperatures of the first 5, middle 8, and last 5nucleotides of the gRNA. After the calculation, the resulting on-targetactivity (azimuth score) can range from 0 and 1.

In some cases, the on-target efficiency value (azimuth score) can be acriterion in selecting a subset of gRNAs from the initial set of gRNAs.The subset of gRNAs from the initial set of gRNAs can have the on-targetefficiency value of at least about 0.2. The subset of gRNAs from theinitial set of gRNAs can have the on-target efficiency value of at leastabout 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, or more. In an example,the subset of gRNAs from the initial set of gRNAs can have the on-targetefficiency value of greater than 0.4.

In some cases, both the on-target efficiency and the off-target value ofa gRNA can be the criteria in selecting a subset of gRNAs from theinitial set of gRNAs. In an example, identifying the subset of gRNAs canbe based on the threshold value of the on-target efficiency (e.g., anazimuth score greater than 0.4) and the threshold value of theoff-target value (e.g., no off-target hybridization sites with 0 or 1mismatches). Based on the two criteria, each gRNA in the initial set ofgRNAs can be ranked. The subset of gRNAs from the initial set of gRNAscan comprise from 1 to 10 of the top ranked gRNAs. The subset of gRNAsfrom the initial set of gRNAs can comprise at least 1, 2, 3, 4, 5, 6, 7,8, 9, 10, or more of the top ranked gRNAs. The subset of gRNAs from theinitial set of gRNAs can comprise at most 10, 9, 8, 7, 6, 5, 4, 3, 2, orless of the top ranked gRNAs.

The initial set of gRNAs, each designed to hybridize a portion of thegene of the genome of the species, can be used to knock-out (KO) thegene in a cell. The KO can be a targeted KO. Alternatively or inaddition to, the initial set of gRNAs, each designed to hybridize theportion of the gene of the genome of the species, can be used toknock-in (KI) a mutation in the gene in a cell. The KI can be a targetedinsertion. The targeted insertion can be an insertion of a donorpolynucleotide. In some cases, at least one CRISPR/Cas complex can bedirected to a target region by at least one specific gRNA and cleave thetarget region. In some examples, the cleavage can lead to insertionand/or deletion (“indel”) mutations or a frameshift by a non-homologousend joining (NHEJ) process, leading to a target gene-specific KO. Insome cases, CRISPR/Cas complex can be directed to the target genomicregion by the specific gRNA along with a co-administered, donorpolynucleotide (single- or double-stranded). Following the cleavage ofthe target region, a homology-directed repair (HDR) process can use oneor more of the donor polynucleotide as one or more templates for (a)repair of the cleaved target nucleotide sequence and (b) a transfer ofgenetic information from the donor polynucleotide to the target DNA.Depending on the nature of the genetic information, the HDR process canyield a target gene-specific KO or KI. Examples of applications of theHDR-mediated gene KI include the addition (insert or replace) of nucleicacid material encoding for a protein, mRNA, small interfering RNA(siRNA), tag (e.g., 6xHis (SEQ ID NO: 91)), a reporter protein (e.g., agreen fluorescent protein), and a regulatory sequence to a gene (e.g., apromotor, polyadenylation signal).

For the HDR process, the donor polynucleotide can contain the desiredgene edit (sequence) to be copied, as well as additional nucleotidesequences on both ends (homology arms) that are homologous immediatelyupstream and downstream of the cleaved target site. In some cases, theefficiency of the HDR process can depend on the size of the gene editand/or the size of the homology arms. Alternatively or in addition to,the efficiency of the HDR process can depend on the availability of Castarget regions that comprise the PAM site. Thus, the methods comprisingdetermining (a) the initial set of RNA sequences from the plurality ofgene and/or genome databases, (b) the on-target efficiency, and/or (c)the off-target value can be utilized to design a set of donorpolynucleotides for HDR.

The CRISPR/cas system according to the present disclosure can be used ina variety of cells. Cells can be any prokaryotic or eukaryotic livingcells, cell lines derived from these organisms for in vitro cultures,primary cells from animal or plant origin. Eukaryotic cells can refer toa fungal, plant, algal or animal cell or a cell line derived from theorganisms listed below and established for in vitro culture. The funguscan be of the genus Aspergillus, Penicillium, Acremonium, Trichoderma,Chrysoporium, Mortierella, Kluyveromyces or Pichia; More preferably, thefungus is of the species Aspergillus niger, Aspergillus nidulans,Aspergillus oryzae, Aspergillus terreus, Penicillium chrysogenum,Penicillium citrinum, Acremonium Chrysogenum, Trichoderma reesei,Mortierella alpine, Chrysosporium lucknowense, Kluyveromyceslactis,Pichia pastoris or Pichia ciferrii. The plant can be of the genusArabidospis, Nicotiana, Solanum, lactuca, Brassica, Oryza, Asparagus,Pisum, Medicago, Zea, Hordeum, Secale, Triticum, Capsicum, Cucumis,Cucurbita, Citrullis, Citrus, Sorghum. The plant can be of the speciesArabidospis thaliana, Nicotiana tabaccum, Solanum lycopersicum, Solanumtuberosum, Solanum melongena, Solanum esculentum, Lactuca saliva,Brassica napus, Brassica oleracea, Brassica rapa, Oryza glaberrima,Oryza sativa, Asparagus officinalis, Pisumsativum, Medicago sativa, Zeamays, Hordeum vulgare, Secale cereal, Triticuma estivum, Triticum durum,Capsicum sativus, Cucurbitapepo, Citrullus lanatus, Cucumis melo, Citrusaurantifolia, Citrus maxima, Citrus medica, and Citrus reticulata. Theanimal cell can be of the genus Homo, Rattus, Mus, Cricetulus, Pan, Sus,Bos, Danio, Canis, Felis, Equus, Salmo, Oncorhynchus, Gallus, Meleagris,Drosophila, Caenorhabditis. The animal cell can be of the species Homosapiens, Rattus norvegicus, Mus musculus, Cricetulus griseus, Panpaniscus, Sus scrofa, Bos taurus, Canis lupus, Cricetulus griseus, Daniorerio, Felis catus, Equus caballus, Rattus norvegecus, Salmo salar,Oncorhynchus mykiss, Gallus gallus, Meleagris gallopavo, Drosophilamelanogaster, and Caenorhabditis elegans.

Examples cell lines can be selected from the group consisting of CHOcells (e.g., CHO-K1); HEK293 cells; Caco2 cells; U2-OS cells; NIH 3T3cells; NSO cells; SP2 cells; DG44 cells; K-562 cells, U-937 cells; MC5cells; IMR90 cells; Jurkat cells; HepG2 cells; HeLa cells; HT-1080cells; HCT-116 cells; Hu-h7 cells; Huvec cells; and Molt 4 cells.Examples of other cells applicable to the scope of the presentdisclosure can include stem cells, embryonic stem cells (ESCs) andinduced pluripotent stem cells (iPSCs). All these cell lines and/orcells can be modified by the method of the present invention to providecell line models to produce, express, quantify, detect, and/or study agene or a protein of interest; and/or to screen biologically activemolecules of interest in research and production and various fields suchas chemical, biofuels, therapeutics and agronomy as non-limitingexamples.

In some cases, at least one nucleotide from at least one guide RNA inthe initial set of guide RNAs can be modified. Examples of themodification of the at least one nucleotide can include: (a) endmodifications, including 5′ end modifications or 3′ end modifications;(b) nucleobase (or “base”) modifications, including replacement orremoval of bases; (c) sugar modifications, including modifications atthe 2′, 3′, and/or 4′ positions; and (d) backbone modifications,including modification or replacement of the phosphodiester linkages.

Not wishing to be bound by theory, the modification of the at least onenucleotide can provide, for example: (a) improved target specificity;(b) reduced effective concentration of the CRISPR/Cas complex; (c)improved stability of the gRNA (e.g., resistance to ribonucleases(RNases) and/or deoxyribonucleases (DNases)); and (d) decreasedimmunogenicity. In an example, the at least one nucleotide from the atleast one guide RNA in the initial set of guide RNAs can be a2′-O-methyl nucleotide. Such modification can increase the stability ofthe gRNA with respect to attack by RNases and/or DNases.

In some cases, a nucleotide sugar modification incorporated into theguide RNA is selected from the group consisting of 2′-O—C₁₋₄alkyl suchas 2′-O-methyl (2′-OMe), 2′-deoxy (2′-H), 2′-C₁₋₃alkyl-O—C₁₋₃alkyl suchas 2′-methoxyethyl (“2′-MOE”), 2′-fluoro (“2′-F”), 2′-amino (“2′-NH₂”),2′-arabinosyl (“2′-arabino”) nucleotide, 2′-F-arabinosyl(“2′-F-arabino”) nucleotide, 2′-locked nucleic acid (“LNA”) nucleotide,2′-unlocked nucleic acid (“ULNA”) nucleotide, a sugar in L form(“L-sugar”), and 4′-thioribosyl nucleotide. In some cases, aninternucleotide linkage modification incorporated into the guide RNA isselected from the group consisting of: phosphorothioate “P(S)” (P(S)),phosphonocarboxylate (P(CH₂)_(n)COOR) such as phosphonoacetate “PACE”(P(CH₂COO⁻)), thiophosphonocarboxylate ((S)P(CH₂)_(n)COOR) such asthiophosphonoacetate “thioPACE” ((S)P(CH₂)_(n)COO⁻)), alkylphosphonate(P(C₁₋₃ alkyl) such as methylphosphonate-P(CH₃), boranophosphonate(P(BH₃)), and phosphorodithioate (P(S)₂).

In some cases, a nucleobase (“base”) modification incorporated into theguide RNA is selected from the group consisting of: 2-thiouracil(“2-thioU”), 2-thiocytosine (“2-thioC”), 4-thiouracil (“4-thioU”),6-thioguanine (“6-thioG”), 2-aminoadenine (“2-aminoA”), 2-aminopurine,pseudouracil, hypoxanthine, 7-deazaguanine, 7-deaza-8-azaguanine,7-deazaadenine, 7-deaza-8-azaadenine, 5-methylcytosine (“5-methylC”),5-methyluracil (“5-methylU”), 5-hydroxymethylcytosine,5-hydroxymethyluracil, 5,6-dehydrouracil, 5-propynylcytosine,5-propynyluracil, 5-ethynylcytosine, 5-ethynyluracil, 5-allyluracil(“5-allylU”), 5-allylcytosine (“5-allylC”), 5-aminoallyluracil(“5-aminoallylU”), 5-aminoallyl-cytosine (“5-aminoallylC”), an abasicnucleotide, Z base, P base, Unstructured Nucleic Acid (“UNA”),isoguanine (“isoG”), isocytosine (“isoC”), and 5-methyl-2-pyrimidine.

In some cases, one or more isotopic modifications are introduced on thenucleotide sugar, the nucleobase, the phosphodiester linkage and/or thenucleotide phosphates. Such modifications include nucleotides comprisingone or more ¹⁵N, ¹³C, ¹⁴C, Deuterium, ³H, ³²P, ¹²⁵I, ¹³¹I, atoms orother atoms or elements used as tracers.

In some cases, an “end” modification incorporated into the guide RNA isselected from the group consisting of: PEG (polyethyleneglycol),hydrocarbon linkers (including: heteroatom (O,S,N)-substitutedhydrocarbon spacers; halo-substituted hydrocarbon spacers; keto-,carboxyl-, amido-, thionyl-, carbamoyl-, thionocarbamaoyl-containinghydrocarbon spacers), spermine linkers, dyes including fluorescent dyes(for example fluoresceins, rhodamines, cyanines) attached to linkerssuch as for example 6-fluorescein-hexyl, quenchers (for example dabcyl,BHQ) and other labels (for example biotin, digoxigenin, acridine,streptavidin, avidin, peptides and/or proteins). In some cases, an “end”modification comprises a conjugation (or ligation) of the guide RNA toanother molecule comprising an oligonucleotide (comprisingdeoxynucleotides and/or ribonucleotides), a peptide, a protein, a sugar,an oligosaccharide, a steroid, a lipid, a folic acid, a vitamin and/orother molecule. In some cases, an “end” modification incorporated intothe guide RNA is located internally in the guide RNA sequence via alinker such as, for example, 2-(4-butylamidofluorescein)propane-1,3-diolbis(phosphodiester) linker, which is incorporated as a phosphodiesterlinkage and can be incorporated anywhere between two nucleotides in theguide RNA.

In some cases, a computer can be utilized to perform the method fordesigning one or more guide RNAs (gRNAs) for hybridizing to a to genomicregion of interest.

FIG. 1 shows an example of a flowchart 100 of a method of designing oneor more guides (e.g., gRNAs) for hybridizing to a gene of a genome of aspecies. The method comprises: (a) getting details on the gene ofinterest from one or more databases 105; (b) locate a target region(e.g., an exon of a selected transcript of a plurality of transcripts ofthe gene) 110; (c) get potential guides (e.g., one or more gRNAs with ahybridizing polynucleotide sequence) 115; (d) compute on-target valuefor each guide (e.g., calculate an azimuth score for each guide) 120;(e) compute off-target hits for each guide 125; (f) get more detailsabout likely off-targets (e.g., enumerating the number of mismatches foreach of the gRNAs as compared to a plurality of possible Cas targetregions across the genome) 130; and (g) return a ranked list of theguides 135. In (g), the guides can be ranked by the on-target valuesand/or the off-target values. In some cases, the order of the steps (d)and (e-f) can be interchangeable.

FIG. 2 shows an example of a table 200 of a plurality of transcripts ofa gene of a genome of a species. The information can be obtained fromone or more databases. In this example, the gene of interest is RELA 220of Homo sapiens 210. A plurality of transcripts 230 are known for RELA.The plurality of RELA transcripts can have different numbers ofnucleotide bases 232. Alternatively or in addition to, the plurality oftranscripts can be translated into polypeptides (e.g., proteins) thathave different numbers of amino acids 234. Furthermore, a transcript canhave a higher abundance reported than one or more other transcripts inthe plurality of transcripts (not shown). Based on one or more of theaforementioned factors, a primary transcript 240 is selected.

FIG. 3 shows an example of an early coding region of a transcript to betargeted by one or more gRNAs. In a plurality of transcripts of thehuman gene RELA, the primary transcript 240 is analyzed to select anearly coding region comprising an exon 310. The exon 310 is also foundin more than 50 percent (%) of the other transcripts of the plurality oftranscripts. Subsequently, all possible Cas target regions across theexon 310 can be identified to design the one or more gRNAs for targetingand hybridizing.

FIG. 4A shows examples of plots of relative representation 410 and exonlength 420 of a plurality of exons from a transcript. The plurality ofexons (positions 1 through 11) are from the primary transcript 240. Theselected exon 310 has a relative representation value of over 0.9,suggesting that it is present in more than 90% of the other transcriptsof the same gene. The selected exon 310 is also over 100 nucleotides inlength.

FIG. 4B shows examples of gRNAs 450, 452, 454, and 460 and theiroff-target and on-target activity analyses. The analysis for each gRNAis summarized as a recommendation bracket [A, B, C, D], where A denoteswhether the gRNA is designed to target an early coding region of a gene;B denotes whether the intended target site of the gene is common in aplurality of transcripts of the gene (i.e., found in over 50% of theother transcripts in the plurality of transcripts of the gene); Cdenotes whether the on-target activity of the gRNA is above a thresholdvalue (i.e., an azimuth score greater than 0.4); and D denotes whetherthe off-target activity of the gRNA is above a threshold value (i.e.,not having any off-target hybridizing region with 0 and 1 mismatch). Allfour factors A through D need to be “True” (as opposed to “False”) forthe gRNA to be selected for use. In FIG. 4B, only the gRNA 460 is deemedTrue for all four factors.

Computer System

Another aspect of the present disclosure provides a computer system forperforming the aforementioned method for designing one or more guideRNAs (gRNAs) for hybridizing to a genomic region of interest. Anotheraspect of the present disclosure provides a computer system forperforming the aforementioned method for designing one or more guideRNAs (gRNAs) for hybridizing to each of a plurality of genomic regionsof interest. The genomic region of interest can be a gene of a genome ofa species. The computer system can comprise a computer readable mediumfor selecting a transcript from a plurality of transcripts of the gene.The computer system can comprise a computer readable medium foridentifying an initial set of gRNAs that hybridize different targetsites in the gene of the selected transcript.

The computer readable medium of the computer can receive (e.g., from auser via a user interface on a user device) input of the gene and thespecies of interest. The computer readable medium can be incommunication with a plurality of databases to obtain informationcomprising the genome of the species and/or a reference genome of thespecies. In some cases, the computer readable medium can be incommunication with the plurality of databases including gene and/orgenome databases comprising sequencing data from DNA (DNA-seq) and/orRNA (RNA-seq). Based on such information, the computer readable mediumcan select the transcript from the plurality of transcripts of the gene.The computer readable medium can identify the initial set of gRNAs thathybridize different target sites in the gene of the selected transcript.Alternatively or in addition to, the computer readable medium can beconfigured to perform one or more tasks related to the aforementionedmethod for designing the one or more gRNAs for hybridizing the gene ofthe genome of the species (e.g., calculating off-target values for theone or more gRNAs). Furthermore, the computer readable medium can alsoinclude instructions for automatically activating a biopolymer (e.g.,RNA) synthesizer as selected by a user of the design tool.

FIG. 5 shows a data processing architecture 500 for calculatingoff-target values of a plurality of gRNAs across a genome. An initialset of gRNAs (or, alternatively, a set of the respective target sitesequences of each gRNA) 510 is entered into a “master” query 520 of acomputing platform (e.g., a serverless computing platform). At the sametime, a database of all possible Cas target regions across the genome(e.g., each domain comprising a protospacer sequence and a PAM site) ispartitioned into smaller subsets (or “shards”) 505. To obtain theoff-target values, the master query 520 invokes additional “slave”queries 525, one per each shard, and compares 530 each slave query toeach shard to determine mismatches and the overall off-target value ofeach gRNA. After the off-target search, the results from slavequery-shard comparisons are collected into a results aggregator 540. Inan example, a database of about 320 million Cas target regions can bepartitioned into 161 shards, each comprising about 2 million Cas targetregions. As such, the master query would invoke 161 slave queries foroff-target search. By using the data processing architecture 500 andsimultaneously running comparisons, the output time of the analyses canbe reduced.

Multiple gRNA Systems

In some embodiments, the present disclosure provides a method foridentifying a set of guide RNAs (gRNAs) that target a genomic region ofinterest. The set of gRNAs can comprise at least 2, at least 3, at least4, at least 5, at least 10, at least 20, at least 50, at least 100, orat least 200 gRNAs. The set of gRNAs can consist of 2 gRNAs. The set ofgRNAs can consist of 3 gRNAs. The set of gRNAs can consist of 4 gRNAs.The method can comprise designing, in a computer, a set of gRNAs. EachgRNA in the set can be hybridizable to a different target site withinthe genomic region of interest (e.g., a gene, gene cluster, exon).

The distance between a target site of each gRNA in a set of gRNAstargeting the same genomic region of interest can also be referred toherein as the inter-guide spacing. The inter-guide spacing can be thedistance, in base pairs, from the 3′ end of a first target site in agenomic region of interest of a first gRNA to the 5′ end of a secondtarget site in the genomic region of interest of a second gRNA in a setof gRNAs. The inter-guide spacing can be non-inclusive of the base pairscomprising the target site in the genomic region of interest of thefirst gRNA and the target site in the genomic region of interest of thesecond gRNA. The inter-guide spacing can be determined based on areference genome. The inter-guide spacing can be determined betweensequential target sites in the genomic region of interest. In anexample, a minimum distance between a target site in a genomic region ofinterest of a gRNA in the set of gRNAs is at least 30 bases apart fromthe target site in the genomic region of interest of at least one othergRNA from the set of gRNAs. In another example, a minimum distancebetween a target site in a genomic region of interest of each gRNA inthe set of gRNAs is at least 30 bases apart from the target site in thegenomic region of interest of every other gRNA from the set of gRNAs. Inanother example, a maximum distance between a target site in a genomicregion of interest of a gRNA in the set of gRNAs is at most 150 basesapart from the target site in the genomic region of interest of at leastone other gRNA from the set of gRNAs. In some embodiments, at least 50%,at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%of the sets of gRNAs in a plurality of sets of gRNAs comprise at least 3gRNAs.

Editing efficiency can indicate the proportion of cells comprising anedited genotype at the genomic region of interest. The cells can be apopulation of cells contacted with at least one set of gRNAs, anuclease, and optionally a donor polynucleotide. The edited genotype canbe any non-wildtype genotype. The edited genotype can comprise aninsertion or deletion relative to a wild-type genotype. The editedgenotype can be a result of repair of a double stranded break caused bya CRISPR/Cas complex at the target site. The edited genotype can resultin a knock-out of the genomic region of interest. In some embodiments, aset of gRNAs with a minimum distance between target regions in thegenomic region of interest of each gRNA in the set of gRNAs of 30 ormore bases produces an editing efficiency of greater than 50%, 60%, 70%,or 80%. In some embodiments, a plurality of sets of gRNAs comprise amean editing efficiency of at least 50%, 60%, 70%, or 80%. In someembodiments, at least 50%, 60%, 70%, 80%, 90%, or 95% of the sets ofgRNA in a plurality of sets of gRNAs comprise a mean editing efficiencyof greater than 50%. In some embodiments, at least 50%, 60%, 70%, 80%,90%, or 95% of the sets of gRNA in a plurality of sets of gRNAs comprisea mean editing efficiency of greater than 70%. The editing efficiencycan be determined by sequencing. The sequencing can be Sangersequencing. The sequencing can be high throughput sequencing.

Each gRNA in the set can be hybridizable to a target site that is atleast 10 bases apart from the target site in the genomic region ofinterest of at least one other gRNA from the set of gRNAs. Each gRNA inthe set can be hybridizable to a target site in the genomic region ofinterest that is at least 30 bases apart from the target site in thegenomic region of interest of at least one other gRNA from the set ofgRNAs. Each gRNA in the set can be hybridizable to a target site in thegenomic region of interest that is at most 170 bases apart from thetarget site in the genomic region of interest of at least one otherguide RNA from the set of guide RNAs. Each gRNA in the set can behybridizable to a target site in the genomic region of interest that isat most 1000 bases apart from the target site in the genomic region ofinterest of at least one other guide RNA from the set of guide RNAs.Preferably the target site in the genomic region of interest of eachgRNA from the set of gRNAs is separated from the target site in thegenomic region of interest of any other gRNA in the set by about 10-170,30-170, 10-150, 30-150, 10-100, 30-100, or 30-1000 bases. Thisarrangement can result in improved KO properties and synergistic effectsbetween the various CRISPR enzymes. In some embodiments, knockout of agenomic region of interest is achieved using an amount of each gRNA in aset of gRNAs less than the amount of each gRNA individually required toachieve knockout of the genomic region of interest. The amount of eachgRNA in a set of gRNAs required to achieve knockout of the genomicregion of interest can be ⅓ of the amount of each gRNA individuallyrequired to achieve knockout of the genomic region of interest. Theamount of each gRNA in a set of gRNAs required to achieve knockout ofthe genomic region of interest can be ½ of the amount of each gRNAindividually required to achieve knockout of the genomic region ofinterest.

Further described herein, in certain embodiments, are methods foridentifying a plurality of sets of guide RNAs (gRNAs), each set of gRNAsin the plurality of sets of gRNAs targeting a different genomic regionof interest in a plurality of genomic region of interests. The pluralityof genomic regions of interest can comprise 2, 3, 4, 5, 6, 7, 8, 9, 10,or more than 10 genomic regions of interest. The method can comprisedesigning, in a computer, a different set of gRNAs for each of theplurality of genomic regions of interest. Each gRNA in the set of gRNAscan be hybridizable to a different target site within the genomic regionof interest (e.g., a gene, gene cluster, exon). Each gRNA in the set ofgRNAs can be hybridizable to a target site in the genomic region ofinterest that is at least 10 bases apart from the target site in thegenomic region of interest of at least one other gRNA from the set ofgRNAs. Each gRNA in the set can be hybridizable to a target site in thegenomic region of interest that is at least 30 bases apart from thetarget site in the genomic region of interest of at least one other gRNAfrom the set of gRNAs. Each gRNA in the set of gRNAs can be hybridizableto a target site in the genomic region of interest that is at most 170bases apart from the target site in the genomic region of interest of atleast one other guide RNA from the set of guide RNAs. Each gRNA in theset of gRNAs can be hybridizable to a target site in the genomic regionof interest that is at most 1000 bases apart from the target site in thegenomic region of interest of at least one other gRNA from the set ofgRNAs. Preferably the target site in the genomic region of interest ofeach gRNA from the set of gRNAs is separated from the target site in thegenomic region of interest of any other gRNA in the set by about 10-170,30-170, 10-150, 30-150, 10-100, 30-100, or 30-1000 bases. Thisarrangement can result in improved KO properties and synergistic effectsbetween the various CRISPR enzymes.

The computer can be the aforementioned computer system for performingthe method for designing one or more gRNAs for hybridizing to a genomicregion of interest. The genomic region of interest can be a gene of agenome of a species. The genomic region of interest can be a non-codingregion of the genome. The non-coding region can be a regulatory element.The regulatory element can be a cis-regulatory element or atrans-regulatory element. The cis-regulatory element can be a promoter,an enhancer, or a silencer. The identified set of gRNAs can be a subsetof the one or more gRNAs for hybridizing the gene of the genome of thespecies. In some cases, one or more gRNAs of the set of gRNAs can be asingle guide RNA (sgRNA). In some cases, one or more gRNAs of the set ofgRNAs can be a complex (e.g., via hydrogen bonds) of a CRISPR RNA(crRNA) segment and a trans-activating crRNA (tracrRNA) segment.

Each gRNA of the set of gRNAs can comprise a polynucleotide sequence (ahybridizing polynucleotide sequence) that hybridizes to the differenttarget site within the genomic region of interest. The hybridizingpolynucleotide sequence of the gRNA can range from 17 to 23 nucleotides.The hybridizing polynucleotide sequence of the gRNA can be at least 17,18, 19, 20, 21, 22, 23, or more nucleotides. The hybridizingpolynucleotide sequence of the gRNA can be at most 23, 22, 21, 20, 19,18, 17, or less nucleotides. In an example, the hybridizingpolynucleotide sequence of the gRNA is 20 nucleotides.

The gene of the genome of the species can have one or more transcripts.In an example, the gene can be transcribed into one or more transcripts.The one or more transcripts can comprise one or more coding regions(i.e., exons) and/or one or more intragenic non-coding regions (i.e.,introns). In some cases, the genomic region of interest can comprise acoding region of the gene. In some cases, the genomic region of interestcan comprise a non-coding region of the gene. In some cases, the genomicregion of interest can comprise a coding region of the gene and anon-coding region of the gene. If the gene is DNA, the coding region ofthe gene can be closer to a promoter (upstream) of the gene than aterminator (downstream) of the gene. If the gene is RNA, the codingregion of the gene can be closer to a 5′ end of the gene than a 3′ endof the gene. In some cases, the genomic region of interest can comprisean exon of the gene. The genomic region of interest can be an earlyposition exon within the gene. The early position exon can be an exonthat is located within the first half of the gene. The early positionexon can be the first, second, third, fourth, fifth, of sixth exon ofthe gene.

The gene of the genome of the species can be one of a family of genes,and the genomic region of interest that is targeted by the set of gRNAscan comprise the family of genes. In an example, the gene is of theNF-κB (Rel) family of genes comprises RELA, RELB, REL, NFKB1, and NFKB2,and the genomic region of interest can be the NF-κB (Rel) family ofgenes comprising the five genes. In another example the peroxiredoxinfamily of genes comprises PRDX1, PRDX2, PRDX3, PRDX4, PRDX5, and PRDX6,and the genomic region of interest can be the peroxiredoxin family ofgenes comprising the six genes.

The gene of the genome of the species can be one of a pseudogene. Thepseudogene can be a processed pseudogene, non-processed pseudogene,unitary pseudogene, and pseudo-pseudogene.

The genomic region of interest that is targeted by the set of gRNAs cancomprise one or more coding regions from the family of genes. Eachcoding region of the one or more coding regions can be represented by(contained in) between 0% to 100% of the family of genes. Each codingregion of the one or more coding regions can be represented by at least0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or more of thefamily of genes. Each coding region of the one or more coding regionscan be represented by at most 100%, 90%, 80%, 70%, 60%, 50%, 40%, 30%,20%, 10%, 5%, or less of the family of genes. In an example, the genomicregion of interest comprises one coding region that is represented inall genes of the family of genes.

In some cases, the genomic region is a continuous polynucleotide segmentof the gene. The genomic region of interest can range from 1,000 basesor nucleotides (1 kb) to 500 kb. The genomic region of interest can beat least 1 kb, 5 kb, 10 kb, 15 kb, 20 kb, 50 kb, 100 kb, 500 kb, ormore. The genomic region of interest can be at most 500 kb, 100 kb, 50kb, 20 kb, 15 kb, 10 kb, 5 kb, 1 kb, or less.

The identified set of gRNAs that target the genomic region of interestcan comprise from 2 to 200 gRNAs. The identified set of gRNAs thattarget the genomic region of interest can comprise at least 2, 3, 4, 5,6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, or moregRNAs. The identified set of gRNAs that target the genomic region ofinterest can comprise at least 2 gRNAs. The identified set of gRNAs thattarget the genomic region of interest can comprise at least 3 gRNAs. Theidentified set of gRNAs that target the genomic region of interest cancomprise at most 200, 100, 90, 80, 70, 60, 50, 40, 30, 0, 15, 10, 9, 8,7, 6, 5, 4, 3, or less gRNAs. The identified set of gRNAs that targetthe genomic region of interest can comprise at most 4 gRNAs. Theidentified set of gRNAs that target the genomic region of interest cancomprise at most 3 gRNAs.

Each gRNA in the set of gRNAs that target the genomic region of interestcan be hybridizable to a target site in the genomic region of interestthat is about 10 to 200 bases (nucleotides) apart from the target sitein the genomic region of interest of the at least one other gRNA fromthe set of gRNAs. Each gRNA in the set of gRNAs that target the genomicregion of interest can be hybridizable to a target site in the genomicregion of interest that is about 30 to 1000 bases (nucleotides) apartfrom the target site in the genomic region of interest of the at leastone other gRNA from the set of gRNAs. Each gRNA in the set of gRNAs thattarget the genomic region of interest can be hybridizable to a targetsite in the genomic region of interest that is at least 30 bases(nucleotides) apart from the target site in the genomic region ofinterest of the at least one other gRNA from the set of gRNAs. Each gRNAin the set of gRNAs can be hybridizable to a target site in the genomicregion of interest that is at least 10, 15, 20, 25, 30, 25, 40, 45, 50,60, 70, 80, 90, 100, 120, 140, 160, 180, 200 or more bases apart fromthe target site in the genomic region of interest of the at least oneother gRNA from the set of gRNAs. Each gRNA in the set of gRNAs can behybridizable to a target site in the genomic region of interest that isat most 2000, 1500, 1000, 500, 200, 180, 160, 140, 120, 100, 90, 80, 70,60, 50, 45, 40, 35, 30, 25, 20, 15, 10, or less bases apart from atarget site in genomic region of interest of the at least one other gRNAfrom the set of gRNAs.

The set of gRNAs can be designed so that the set of gRNAs directs a setof CRISPR/Cas complexes to different target sites within the genomicregion of interest in a cell. The CRISPR/Cas complex can create a breakin the nucleic acid sequence at the target site. The break can be adouble stranded break. The break can be a single stranded break In anexample, the set of gRNAs can be designed to direct the set ofCRISPR/Cas complexes to knock-out (KO) one or more of the differenttarget sites within the genomic region of interest in the cell. Theknock-out can occur as a result of a frameshift mutation introduced byrepair of a break caused by the CRISPR/Cas complex. The knock-out canoccur as a result of deletion of an exon in a gene of interest. Theknock-out can occur as a result of deletion of at least 1, at least 2,at least 3, at least 4, at least 5, at least 6, at least 7, at least 8,at least 9, at least 10, at least 100, at least 1000, or at least 10,000base pairs in the genomic region of interest. The knock-out caneliminate the function of the gene.

In another example, the set of gRNAs can be designed to direct the setof CRISPR/Cas complexes knock-in (KI) one or more mutations in thedifferent target sites within the genomic region of interest in thecell. The set of CRISPR/Cas complexes can be co-administered with a setof donor polynucleotides for the KI. The knock-in one or more mutationscan introduce a point mutation, an allele, a tag, or an exogenous exoninto the genome. The point mutation, allele, tag or exogenous exon canbe located on the donor polynucleotide. The donor polynucleotide can beincorporated into the genome using homology directed repair (HDR), asdescribed herein. The knock-in one or more mutations can restorefunction of a previously non-functional gene. The knock-in one or moremutations can improve a function of a gene. The improvement of thefunction of the gene can be an increase in an amount of a proteinproduced by the gene. The knock-in can knock-out the function of gene.

In some embodiments, the knock-in one or more mutations can introduce atag into the genome. The tag can be a detectable tag. The detectable tagcan be a fluorescent tag. The detectable tag can be a restrictionfragment length polymorphism (RFLP).

In some embodiments, the knock-in one or more mutations can introduce apoint mutation into the genome. The point mutation can be an insertion,a deletion, or a substitution of a nucleic acid in the genomic region ofinterest. In some embodiments, the knock-in one or more mutations canintroduce an allele into the genome. The allele can be a transgene.

In some embodiments, the knock-in one or more mutations can introduce anexogenous exon into the genome. The exon can be at least 80%, 85%, 90%,95%, or 99% identical to an endogenous exon of the target gene. Theendogenous exon can be a wild-type exon. The exogenous exon can be anexon comprising at least one mutation relative to the endogenous exon ofthe target gene. In some embodiments, the knock-in one or more mutationscan replace an endogenous exon with an exogenous exon. The exogenousexon can comprise at least one mutation relative to the wild-type exon.The exogenous exon can be in a donor polynucleotide.

In some embodiments, the method further comprises designing at least asecond initial set of gRNAs to create a plurality of initial sets ofgRNAs. The method can comprise identifying a plurality of initial setsof gRNAs targeting a plurality of genes, wherein each initial set ofgRNA in the plurality of initial sets of gRNAs hybridizes differenttarget sites in a gene in the plurality of genes.

Kit of Multiple gRNAs

Another aspect of the present disclosure provides a kit comprising aplurality of gRNAs generated by the aforementioned methods foridentifying a set of guide RNAs (gRNAs) that target a genomic region ofinterest. The kit can comprise a set of gRNAs. Each gRNA in the set canbe hybridizable to a different target site within the genomic region ofinterest. Each gRNA in the set can be hybridizable to a target site inthe genomic region of interest that is at least 10 bases apart from thetarget site in the genomic region of interest of at least one other gRNAfrom the set of gRNAs. Each gRNA in the set can be hybridizable to atarget site in the genomic region of interest that is at least 30 basesapart from the target site in the genomic region of interest of at leastone other gRNA from the set of gRNAs. Each gRNA in the set can behybridizable to a target site in the genomic region of interest that isat most 170 bases apart from the target site in the genomic region ofinterest of at least one other guide RNA from the set of guide RNAs.Each gRNA in the set can be hybridizable to a target site in the genomicregion of interest that is between 30 and 1000 bases apart from thetarget site in the genomic region of interest of every other guide RNAfrom the set of guide RNAs. In some embodiments, the set of gRNAscomprises at least 2, at least 3, at least 4, at least 5, at least 10,at least 15, at least 20, or at least 30 gRNAs.

In some embodiments, the kit comprises one set of gRNAs for each of aplurality of genomic regions of interest. The kits described herein canbe used to knock-out the genomic region of interest or the plurality ofgenomic regions of interest. The kits described herein can be used tointroduce a donor polynucleotide into the genomic region of interest.The kits described herein can be used to introduce a plurality of donorpolynucleotides into the plurality of genomic region of interest.

In some embodiments, the kit comprises at least one donorpolynucleotide. In some embodiments, the kit comprises at least onedonor polynucleotide for each of a plurality of genomic regions ofinterest. In some embodiments, the kit comprises a nuclease. Thenuclease can be a Cas protein. The Cas protein can be any Cas proteindescribed herein, such as, for example, Cas9, C2c1, C2c3, or Cpf1. Insome embodiments, the kit comprises a reagent, such as a buffer. Thebuffer can be a Tris buffer, Tris-EDTA (TE) buffer, Tris/Borate/EDTA(TBE) buffer, or Tris-acetate-EDTA (TAE) buffer. The kit can compriseRNAase-free H₂O. In some embodiments, the kit comprises a transfectionreagent. Examples of transfection agents include, but are not limitedto, Lipofectamine™ and Oligofectamine™.

In some embodiments, the kit comprises a carrier, package, or containerthat is compartmentalized to receive one or more containers such asvials, tubes, and the like, each of the container(s) comprising one ofthe separate elements to be used in a method described herein. Suitablecontainers include, for example, bottles, vials, syringes, and testtubes. In some embodiments, the container is formed from a variety ofmaterials such as glass or plastic. The kit can comprise a multi-wellplate. The multi-well plate can be a 4-well plate, a 6-well plate, a12-well plate, a 24-well plate, a 48-well plate, a 96-well plate, or a384-well plate. In some embodiments, each well in the multi-well platecomprises one gRNA. In some embodiments, each well in the multi-wellplate comprises one set of gRNAs targeting a single genomic region ofinterest. In some embodiments, each well in the multi-well platecomprises a plurality of gRNAs targeting a plurality of genomic regionsof interest.

In some embodiments, a kit comprises one or more additional containers,each with one or more of various materials (such as reagents, optionallyin concentrated form, and/or devices) desirable from a commercial anduser standpoint for use of described herein. Non-limiting examples ofsuch materials include, but not limited to, buffers, primers, enzymes,diluents, filters, carrier, package, container, vial and/or tube labelslisting contents and/or instructions for use and package inserts withinstructions for use. In some cases, a set of instructions is included.In some cases, a label is on or associated with the container. The labelcan be on a container when letters, numbers or other characters formingthe label are attached, molded or etched into the container itself. Thelabel can be associated with a container when it is present within areceptacle or carrier that also holds the container, e.g., as a packageinsert. The label can be used to indicate that the contents are to beused for a specific therapeutic application. The label can indicatedirections for use of the contents, such as in the methods describedherein.

Another aspect of the present disclosure provides a kit comprising asingle gRNA generated by the aforementioned methods for identifying aguide RNAs (gRNA) that targets a genomic region of interest. The gRNAcan be hybridizable to a target site within a genomic region ofinterest.

Another aspect of the present disclosure provides a kit comprising aplurality of modified cells comprising a modification at a genomicregion of interest. The plurality of modified cells can be produced bycontacting a plurality of cells with a set of gRNAs generated by theaforementioned methods for identifying a set of guide RNAs (gRNAs) thattarget the genomic region of interest, in combination with a nucleaseand optionally a donor polynucleotide.

Computer System Algorithm

Another aspect of the present disclosure provides a computer systemcomprising algorithm for performing the aforementioned method foridentifying a set of guide RNAs (gRNAs) that target a genomic region ofinterest. The algorithm can comprise a step of identifying a set ofgRNAs. The algorithm can comprise a step of identifying a set of gRNAsfor each of a plurality of genomic regions of interest. Each gRNA in theset can be hybridizable to a different target site within the genomicregion of interest. Each gRNA in the set can be hybridizable to a targetsite in the genomic region of interest that is at least 10 bases apartfrom the target site in the genomic region of interest of at least oneother gRNA from the set of gRNAs. Each gRNA in the set can behybridizable to a target site in the genomic region of interest that isat least 30 bases apart from the target site in the genomic region ofinterest of at least one other gRNA from the set of gRNAs. Each gRNA inthe set can be hybridizable to a target site in the genomic region ofinterest that is at least 30 bases apart from the target site in thegenomic region of interest of at all other gRNA from the set of gRNAs.Each gRNA in the set can be hybridizable to a target site in the genomicregion of interest that is at most 170 bases apart from the target sitein the genomic region of interest of at least one other guide RNA fromthe set of guide RNAs. Each gRNA in the set can be hybridizable to atarget site in the genomic region of interest that is between 30 and1000 bases apart from the target site in the genomic region of interestof every other guide RNA from the set of guide RNAs.

Calculating an Off-Target Efficiency

Another aspect of the present disclosure provides a method for selectingat least one guide RNA (gRNA) for hybridizing to a genomic region ofinterest. The genomic region of interest can be a gene of a genome of aspecies. The method can comprise, for each of a plurality of gRNAs of aninitial set of gRNAs that hybridize to the gene, calculating anoff-target value by enumerating a number of mismatches to potential gRNAhybridizing sites in the genome.

The method can utilize the aforementioned computer system comprising thecomputer readable medium for performing the method for designing one ormore gRNAs for hybridizing to a genome region of interest. The genomicregion of interest can be a gene of a genome of a species. The computerreadable medium can calculate the off-target value by enumerating thenumber of mismatches to potential gRNA hybridizing sites in the genome.

In some cases, the computer readable medium of the computer system cancalculate the off-target value and organize the number of mismatches inshards. When calculating the off-target value of the initial set ofgRNAs, the database comprising all possible nuclease (e.g., Casnuclease) target regions across the genome and/or the reference genomeof the species can be partitioned (divided) into a plurality of “shards”(subsets) of the possible Cas nuclease target regions. The initial setof gRNAs can be compared to each of the shards to enumerate mismatchesof 0, 1, 2, 3, and/or 4. In contrast to comparing the initial set ofgRNAs to one database comprising all the possible Cas nuclease targetregions, simultaneously comparing the initial set of gRNAs to each ofthe shards comprising the subsets of the possible Cas nuclease targetregions can improve throughout, speed, and overall performance ofcalculating the off-target value.

The off-target value can be determined over 100,000 base pairs (bp) ornucleotides to 3,000,000,000 bp of a reference genome or across areference genome. The off-target value can be determined over at least100,000 bp, 500,000 bp, 1,000,000 bp, 5,000,000 bp, 10,000,000 bp,50,000,000 bp, 100,000,000 bp, 500,000,000 bp, 1,000,000,000 bp,2,000,000,000 bp, 3,000,000,000 bp, or more of the reference genome oracross the reference genome. The off-target value can be determined overat most 3,000,000,000 bp, 2,000,000,000 bp, 1,000,000,000 bp,500,000,000 bp, 100,000,000 bp, 50,000,000 bp, 10,000,000 bp, 5,000,000bp, 1,000,000 bp, 500,000 bp, 100,000 bp, or less of the referencegenome or across the reference genome. In an example, the off-targetvalue can be determined over 1,000,000 bp of the reference genome oracross the reference genome.

The database comprising the possible nuclease (e.g., Cas nuclease)target regions of a plurality of genomes and/or reference genomes canhave from 1,000 to 1,000,000 nuclease binding sites. The database canhave at least 1,000, 10,000, 50,000, 100,000, 150,000, 200,000, 250,000,300,000, 350,000, 400,000, 450,000, 500,000, 550,000, 600,000, 650,000,700,000, 750,000, 800,000, 850,000, 900,000, 950,000, 1,000,000, or morenuclease binding sites. The database can have at most 1,000,000,950,000, 900,000, 850,000, 800,000, 750,000, 700,000, 650,000, 600,000,550,000, 500,000, 450,000, 400,000, 350,000, 300,000, 250,000, 200,000,150,000, 100,000, 50,000, 10,000, 1,000, or less nuclease biding sites.

The database comprising the possible nuclease (e.g., Cas nuclease)target regions of a plurality of genomes and/or reference genomes canhave from 10 million to 300 million nuclease binding sites. The databasecan have at least 10 million, 25 million, 50 million, 75 million, 100million, 125 million, 150 million, 175 million, 200 million, 225million, 250 million, 275 million, 300 million, or more nuclease bindingsites. The database can have at most 300 million, 275 million, 250million, 225 million, 200 million, 175 million, 150 million, 125million, 100 million, 75 million, 50 million, 25 million, 10 million, orless nuclease biding sites.

Personalized Therapeutics

Another aspect of the present disclosure provides a method for designingone or more guide RNAs (gRNAs) for hybridizing to a genomic region ofinterest in an individual. The method can comprise using theindividual's genome to determine gRNA target site potentials. The methodcan comprise determining, for each gRNA target site potential, anoff-target value for a prospective guide RNA. The method can compriseidentifying one or more gRNAs with an improved utility index.

In some cases, the method can comprise using a genome of a population ofindividuals to determine gRNA target site potentials. Examples of thepopulation of individuals include a set of individuals in a range ofages (e.g., teenager, 65 years or older, etc.), a set of individualsdiagnosed with the same condition (e.g., a patient population withmuscular dystrophy, Parkinson's disease, etc.), a set of individualsundergoing the same disease treatment (e.g., a cohort of subjectsdiagnosed and/or treated for breast cancer, prostate cancer, etc.), etc.Such method can identify gRNA target site potentials for eachindividual, a subset of individuals from the population of individuals,and/or the whole population of individuals.

In some cases, the software and methods described herein are used tomake a selection and/or recommendation of a gRNA that can be used in aCRISPR system across a patient cohort and/or in a specific patientdemographic. For example, a therapeutic agent comprising a CRISPR systemwith an activated or deactivated endonuclease can be administered to asubject that has been selected using the methods and systems herein. Adetermination that a gRNA would result in a number of off-target bindingexceeding a threshold would result in lack of selection of the patientfor treatment or a recommendation of another treatment. A determinationthat a gRNA would result in a number of off-target binding less than athreshold would result in selecting the patient for treatment orrecommending such a treatment to the patient.

In some instances, any of the methods and systems herein are used foridentifying a gRNA that is present in one, some, or all subjects in apopulation or that is capable of binding to target site in one, some orall subjects in a population, preferably with a reduced off-targetvalue. This would also cover the calculation of the off-target valuesfor a selected gRNA across all subjects in the population.

A gRNA designed from a reference assembly can be evaluated usinginformation derived from a clinical study as described herein or usinggenomic information from a plurality of individuals (e.g., at least 10,100, 1,000, 10,000, or 100,000). For example, a clinical study caninvolve sequencing the genome of a set of subjects having a condition tobe treated and/or normal, and determining the off-target values for atest gRNA across the genomes of the above individuals. In some cases, agRNA designed from a single reference genome (e.g., an individual) canbe evaluated for its off-target activity across one, some, or allsubjects in a population. This can be done for example, for designing anew therapeutic agent using a reference genome and evaluating itspossible efficiency or efficacy in a subject, set of subjects or acrossa population or demographic of individuals using the methods and systemsherein. The gRNA can be further modified to increased stability, pK,delivery, reduced off-target value or reduced off-target affect.

In one instance, a clinical study can involve sequencing the genome ofat least one subject in a set of subjects having a condition to betreated and/or normal, designing a gRNA based on the genome of the atleast one subject, and determining the off-target values for thedesigned gRNA across one, some, or all subjects in the set of subjects.

The method for designing the one or more gRNAs for hybridizing thegenomic region of the individual can utilize the aforementioned methodfor designing one or more gRNAs for hybridizing to a genomic region ofinterest. The genomic region of interest can be a gene of a genome of aspecies. The method can further comprise receiving the genome of theindividual and/or the genome of the population of individuals (e.g.,from user input via a user interface on a user device, or from adatabase). The method can further comprise identifying, from the genome,all possible target regions (or target loci) that comprise a protospacer(target site), a protospacer adjacent motif (PAM) that is recognized byone or more types of Cas enzymes, and the opposite strand of theprotospacer (binding site). The method can further comprise isolatingthe identified target regions in the genomic region of interest. Theisolated target regions can be the target site potentials. In anexample, the genomic region of interest can be a specific site within aT-cell genome of an immune cell of the individual or the population ofindividuals to reduce the risk of gene insertion at incorrect orundesired locations.

The method can further comprise identifying an initial set of gRNAs thathybridize the target site potentials of the individual or the populationof individuals. The method can further comprise calculating anoff-target value and/or an on-target efficiency of each gRNA of theinitial set of gRNAs to determine the utility index of each gRNA. Insome cases, the utility index can be a therapeutic index. In some cases,the therapeutic index comprises reduction of off-target binding that canbe assessed by the off-target value and/or the on-target efficiency. Assuch, in some cases, different threshold values of the off-target valueand the open target efficiency can be used to identify the one or moregRNAs with an improved utility index. Thus, the method can furthercomprise editing a cell with the one or more gRNAs with the improvedutility index.

In some cases, the therapeutic index comprises reduction of not onlyoff-target binding, but also increased on-target efficiency, increasedknock-out (KO) efficiency, increased knock-in (KI) efficiency, ormodulation of CRISPR interference in at least one cell of the individualor the population of individuals. In an example, the one or more gRNAscan be designed to KO a gene in the genomic region of a cell of theindividual or the population of individuals. In another example, the oneor more gRNAs can be designed to KI a mutation in the genomic region ofa cell of the individual or the population of individuals.

In some cases, the individual can be a human. In some cases, theindividual can be a non-human (e.g., a mouse, rat, etc.) In some cases,the individual (or the individuals in the population of individuals) canbe afflicted with a condition. The condition can be known or predictedto be related to a number of disease-associated genes, and the one ormore gRNAs of the present disclosure can direct one or more CRISPR/Cassystems to the number of disease-associated genes. The number ofdisease-associated genes can comprise any gene or polynucleotide whichis yielding transcription or translation products at an abnormal levelor in an abnormal form in cells derived from a disease-affected tissuescompared with tissues or cells of a non-disease control. An example canbe a gene that becomes expressed at an abnormally high level. Anotherexample can be a gene that becomes expressed at an abnormally low level,where the altered expression correlates with the occurrence and/orprogression of the disease. Alternatively or in addition to, the numberof disease-associated genes can comprise any gene possessing mutations.

Examples of the number of disease-associated genes include Alzheimer'sdisease, Parkinson's disease, multiple sclerosis, spinal musculardystrophy, muscular dystrophy, diseases affecting myeloid cells, chroniclymphocytic leukemia, multiple myeloma, malignant tumors, melanomas,cystic fibrosis, hemophilia, sickle cell disease, and cancers of variousorgans including breast, intestine, prostate, central nervous system,glioblastoma, and sarcoma.

The method of determining of the gRNA target site potentials and theidentifying of the one or more gRNAs can be performed by a computer. Thecomputer can be the aforementioned computer system comprising thecomputable medium for performing the method for designing one or moregRNAs for hybridizing to a genomic region of interest.

Personalized Diagnostics

Another aspect of the present disclosure provides a method for assessingoff-target effect of a CRISPR agent on an individual. The methodcomprises, using the individual's genome, determining by a computer theoff-target value of the CRISPR agent by enumerating a number ofmismatches to potential target sites in the individual's genome.

This can be useful, for example, in clinical trial settings, to selectpatients to be included or excluded from a clinical trial, or treatment.For example, a patient whose personal genome has a larger number ofoff-target binding sites than a threshold value (e.g., 0, 1, 2, 3, etc)is excluded from a clinical trial or from a treatment regimen, while apatient whose personal genome has a smaller or no off-target bindingsites is included in the clinical trial or receives treatment.

The method for assessing off-target effect of the CRISPR agent on theindividual can utilize the aforementioned computer system comprising thecomputer readable medium for performing the method for designing one ormore gRNAs for hybridizing to a genomic region of interest. The genomicregion of interest can be a gene of a genome of a species. The methodcan further comprise outputting a report that enumerates the number ofmismatches to potential target sites in the individual's genome. In somecases, the outputting can be displayed on a screen (e.g., via a userinterface on a user device such as a personal computer).

The CRISPR agent can be a therapeutic agent. The therapeutic agent canbe a gRNA from the one or more gRNAs for hybridizing the gene of thegenome of the species. The gRNA can direct a CRISPR/Cas complex to atarget region. The therapeutic agent can have a wide variety of utilityincluding modifying (e.g., deleting, inserting, translocating,inactivating, activating) a target region in a multiplicity of celltypes. As such, the therapeutic agent can have a broad spectrum ofapplications including, but are not limited to, gene therapy, drugscreening, disease diagnosis, and prognosis.

The number of potential target sites in the individual's genome canrange from 1,000 to 3,000,000. The number of potential target sites inthe individual's genome can be at least 1,000, 10,000, 100,000, 200,000,300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000,1,000,000, 2,000,000, 3,000,000, or more. The number of potential targetsites in the individual's genome can be at most 3,000,000, 2,000,000,1,000,000, 900,000, 800,000, 700,000, 600,000, 500,000, 400,000,300,000, 200,000, 100,000, 10,000, 1,000, or less.

High Efficiency and Precision Methods for Editing

An aspect of the present disclosure provides a method for editing a cellor a population of cells, comprising: contacting a cell or population ofcells with the one or more sets of gRNAs, in combination with a nucleaseand optionally a donor polynucleotide, to produce a population ofmodified cells. The population of modified cells can comprise at leastone edit in at least one genomic region of interest. The one or moresets of gRNA can be designed by any of the methods described herein. Theat least one edit can result in a knock-out of a gene in the genomicregion of interest or a knock-in of a point mutation, an allele, a tag,or an exogenous exon into the genome at the genomic region of interest.Another aspect of the present disclosure provides a method for screeninga cell or a population of modified cells comprising at least one edit inat least one genomic region of interest produced by at least one set ofgRNAs. The editing efficiency of one or more sets of gRNA comprising atleast two gRNAs can be higher than an individual editing efficiency ofeach of the at least two gRNAs.

In some embodiments, the method for editing a plurality of genomicregions of interest in a population of cells, comprises: contacting thepopulation of cells with: (i) a plurality of sets of gRNA targeting aplurality of genomic regions of interest and (ii) a nuclease; whereinafter the contacting at least 50% of the cells in the population ofcells comprise an edited genotype different from a wild-type genotype ateach of the genomic regions of interest. In some embodiments, a methodfor editing a plurality of genomic regions of interest in a populationof cells, comprises contacting each of a subset of the population ofcells with: (i) a set of gRNAs from a plurality of sets of gRNAs, whereeach set of gRNAs in the plurality of sets of gRNAs targets a differentgenomic region of interest from the plurality of genomic regions ofinterest and (ii) a nuclease. In some embodiments, after the contactingat least 80% of the cells in at least 50% of the subsets of thepopulation of cells comprise an edited genotype different from awild-type genotype at the genomic region of interest. In someembodiments, after the contacting at least 70% of the cells in each ofthe subsets of the population of cells comprise an edited genotypedifferent from a wild-type genotype at the genomic region of interest.Each set of gRNA can comprise three gRNAs. In some cases, at least 50%,60%, 60%, 70%, 80%, 90%, or 95% of the sets of gRNAs comprises at leastthree gRNAs. Each gRNA in a set of gRNA can comprise an inter-guidespacing of at least 30 bases. Each gRNA in a set of gRNAs can behybridizable to a target site in the genomic region of interest that isat least 30 bases from the target site of all other gRNA from the set ofgRNAs.

The method can comprise designing one or more sets of guide RNAs (gRNAs)for hybridizing to a genomic region of interest, as described herein.The population of modified cells can comprise at least one cell. The atleast one cell can be a mammalian cell, a fish cell, an insect cell, aplant cell, or a microorganism. The microorganism can be a bacterium.The at least one cell can be a cell in a cell line as described herein.The at least one cell can be a tumor cell. The at least one cell can bederived from an individual.

The method can comprise contacting a cell or population of cells withthe one or more sets of gRNAs and a nuclease, to produce a modified cellor a population of modified cells. The method can further comprisecontacting the cell or population of cells with a donor polynucleotide.The contacting can comprise transfecting the one or more sets of gRNAs,the nuclease or a polynucleotide encoding the nuclease, or thecombination thereof into the cell or population of cells. In someembodiments, the each gRNA in the one or more sets of gRNAs is complexedwith a Cas protein prior to the transfecting, to produce a Cas-gRNAcomplex, also referred to herein as CRISPR/Cas complex or CRISPR/Cassystem. In some embodiments, the method further comprises transfectingat least one donor polynucleotide into the cell or population of cells.The transfecting can be a nonviral transfection or viral transfection.The nonviral transfection can be electroporation, lipofection, ormicroinjection. The viral transfection can comprise the use of a viralvector. The viral vector can be a retroviral vector, an adenoviralvector, an adeno associated virus (AAV) vector, an alphavirus vector, avaccinia virus vector, a herpes simplex virus (HSV) vector, a lentivirusvector, or a retrovirus vector. The viral vector can be areplication-competent viral vector or a replication-incompetent viralvector.

The genomic region of interest can be a gene. The gene can be a gene ina pathway of interest. The method can comprise targeting a plurality ofgenomic regions of interest. The plurality of genomic regions ofinterest can comprise a plurality of genes in a pathway of interest. Theplurality of genomic regions of interest can comprise a plurality ofgenes in a plurality of pathways of interest. The pathway of interestcan be a metabolic pathway, a signal transduction pathway, or agene-regulation pathway. The pathway of interest can be a pathwayinvolved in a disease. The disease can be a cancer. The pathway ofinterest can be a pathway involved in production of a molecule ofinterest. The molecule of interest can be a molecule withpharmacological activity. The genomic region of interest can be anon-coding region of the genome. The non-coding region can be aregulatory element. The regulatory element can be a cis-regulatoryelement or a trans-regulatory element. The cis-regulatory element can bea promoter, an enhancer, or a silencer.

In some embodiments, the method comprises contacting a set of gRNAstargeting a genomic region of interest with a subset of the populationof modified cells. In some embodiments, the method comprises contactingeach of a plurality of sets of gRNAs targeting a genomic region ofinterest with each of a plurality of subsets of the population ofmodified cells. In one example, a plurality of subsets of the populationof modified cells can be placed in each well of a multi-well plate. Themulti-well plate can be a 4-well plate, a 6-well plate, a 12-well plate,a 24-well plate, a 48-well plate, a 96-well plate, or a 384-well plate.The subset of the population of modified cells can comprise at least10², at least 10³, at least 10⁴, at least 10⁵, or at least 10⁶ cells.Each well of the multi-well plate can further comprise a set of gRNAtargeting the genomic region of interest. Each set of gRNAs in each wellof the multi-well plate can target different genomic regions ofinterest. The plurality of sets of gRNAs can target at least 5, at least10, at least 20, at least 50, or at least 100 different genomic regionsof interest. In some embodiments, the contacting occurs in each well ofthe multi-well plate.

In some embodiments, the method comprises contacting the population ofmodified cells or subset of the population of modified cells with astimulus. The stimulus can be an additional agent. The additional agentcan be a therapeutic agent (for example: an antibiotic, biologic, or asmall molecule drug) or an agent to induce a disease state in themodified cell.

In some embodiments, the method comprises detecting a phenotype of thepopulation of modified cells or subset of the population of modifiedcells. The phenotype can be cell viability. The phenotype can be anediting efficiency of the set of gRNAs. The phenotype can be an amountof a molecule of interest produced by the population of modified cellsor subset of the population of modified cells. The molecule of interestcan be a protein or a transcript encoding a protein. In someembodiments, the method comprises detecting a tag of the population ofmodified cells or subset of the population of modified cells.

Validating a gRNA

The other aspect of the present disclosure provides a method forvalidating a prospective gRNA. The method can comprise determining anumber of off-target hits for the prospective gRNA in a genome or partof the genome. The method can comprise, using the number of off-targethits, calculating an off-target value for the prospective gRNA. Themethod can comprise predicting activity of the prospective gRNA usingthe off-target value.

The method for validating the prospective gRNA can utilize theaforementioned method for designing one or more gRNAs for hybridizing toa genomic region of interest. The genomic region of interest can be agene of a genome of a species. Alternatively or in addition to, themethod for validating the prospective gRNA can further utilize theaforementioned computer system comprising the computer readable mediumfor performing (1) the method for designing one or more gRNAs forhybridizing to a genomic region of interest, (2) the method foridentifying a set of gRNAs that target a genomic region of interest, and(3) the method for selecting at least one gRNA for hybridizing to agenomic region of interest.

FIG. 6 shows an example of a flowchart 600 of a method of validating oneor more guides (e.g., gRNAs) for hybridizing to a gene of a genome of aspecies. The method comprises: (a) confirming that the guide exists(e.g., confirming that a complimentary sequence of the gRNA provided bya user exists in the genome of the species) 605; (b) look cutinformation (e.g., confirm that the complementary sequence of the gRNAin the genome is next to a protospacer adjacent motif (PAM) site) 610;(c) compute on-target value for the gRNA (e.g., calculate an azimuthscore for the gRNA) 615; (d) compute off-target hits for each guide 620;(e) get more details about likely off-targets (e.g., enumerating thenumber of mismatches for each of the gRNAs as compared to a plurality ofpossible Cas target regions across the genome) 625; and (f) return aprediction of the gRNA activity 630. In some cases, the order of thesteps (c) and (d-e) can be interchangeable.

User Interface

The other additional aspect of the present disclosure provides acomputer system. The computer system can comprise a user interfacesystem for selecting of a species of interest and a gene of interestfrom the species of interest. The computer system can comprise a designmodule integrated with the user interface for identifying one or moresmall guide RNA (gRNA) sequences for the gene of interest. The computersystem can comprise an output system for displaying selected small gRNAsor gRNAs comprising the small gRNAs. Each small gRNA can be about 20bases or nucleotides of each gRNA. The computer system can comprise anactivation unit for initiating synthesis by an RNA synthesizer of theone or more small gRNAs.

The design module of the computer system can perform the aforementionedmethod for validating the prospective gRNA can utilize theaforementioned method for designing one or more gRNAs for hybridizing toa genomic region of interest.

The user interface system can include a selection of 100 to 500,000different reference genomes. The user interface system can include aselection of at least 100, 1,000, 10,000, 100,000, 500,000, or moredifferent reference genomes. The user interface system can include aselection of at most 500,000, 100,000, 10,000, 1,000, 100, or lessdifferent reference genomes. The different reference genomes can bestored in the cloud (e.g., one or more databases in Amazon Web ServicesCould). The design module of the computer system can have accesses tothe reference genomes in the cloud.

The design module of the computer system can have accesses to between10,000 to 120,000 reference genomes. The design module of the computersystem can have accesses to at least 10,000, 20,000, 30,000, 40,000,50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 110,000, 120,000, ormore reference genomes. The design module of the computer system canhave accesses to at most 120,000, 110,000, 100,000, 90,000, 80,000,70,000, 60,000, 50,000, 40,000, 30,000, 20,000, 10,000, or lessreference genomes.

The user interface of the computer system can further comprise a genomicdata receiving module for obtaining input of an individual's genome. Thegenomic data receiving module can obtain the individuals genome or partof the user's genome from a server (e.g., a personal genomic service'sserver) or from a file uploaded by a user.

FIG. 7A-D illustrate examples of a window of a graphical user interface(GUI) for selecting a gene of a genome of a species of interest torequest designing of one or more gRNAs for hybridizing the gene. The oneor more gRNAs can be designed to direct a CRISPR/Cas system for knockoutof the gene of interest. FIG. 7A shows the window of the GUI 700 forKnockout Guide Design that allows the user to type in the name or theidentifier number of the genome 710 and the gene 720 of interest. Inthis GUI, the nuclease of use 730 is pre-selected to be Streptococcuspyogenes Cas9 (SpCas9). In some cases, the user can select a Cas enzymeof interest. FIG. 7B shows a window the GUI for Knockout Guide Design700. When the user types in a portion of the binomial nomenclature ofthe genome of interest (e.g., “Homo”), the software of the GUI 700 cansuggest to the user a list of available genomes that comprise the word“Homo” in the genus (e.g., Homo sapiens, 712) or in the species(Lactobacillus homohiochii, 714). The software of the GUI 700 can alsosuggest different types of the same genome (e.g., Homo sapiens genomefrom “Genecode Release 26” 712 and Homo sapiens genome from “GenecodeRelease 21” 716). The user can then select the correct binomialnomenclature of interest. Alternatively or in addition to, the user cantype in the binomial nomenclature of interest in full. FIG. 7C shows awindow of the GUI for Knockout Guide Design 700. When the user types ina portion of the abbreviation and/or full name of the gene of interest(e.g., “RE”), the software of the GUI 700 can suggest to the user a listof available genes that comprise the typed input (e.g., “RELA” 722 or“ALYREF” 724). The user can then select the correct gene of interest.Alternatively or in addition to, the user can type in the name of thegene of interest in full. FIG. 7D shows a window of the GUI for KnockoutGuide Design 700. Once the genome 710, the gene 720, and the nuclease730 are selected, the user can click the search button 740 to direct thesoftware of the GUI to initiate the method for designing one or moregRNAs for hybridizing the gene of the genome of the species of interest.

FIG. 8 illustrates an example of a window 800 of the GUI for displayinga progress of designing one or more gRNAs for hybridizing the gene ofthe genome of interest. The window 800 shows a list of steps of themethod for designing the one or more gRNAs. The window can show theprogress by marking the steps that have been employed 810 and leavingthe remaining steps unmarked 820.

FIG. 9A-D illustrate examples of a window of the GUI for displaying oneor more gRNAs that are designed to hybridize the gene of the genome ofinterest. FIG. 9A shows a window 900 of the GUI. The window 900 providesa summary 910 of the results of designing the one or more gRNAs (e.g., anumber of Cas target sites in the gene of interest, a number of topranked gRNAs that can be used for the gene knock-out, etc.). The window900 provides hybridizing polynucleotide sequences of the top rankedgRNAs 920. The window 900 also provides a schematic of a selected codingregion 930 of the gene (e.g., exon 3 of the RELA gene) that was used togenerate the one or more gRNAs, as well as the hybridizing locations 940of the top ranked gRNAs 920 within the selected region 930 of the gene.FIG. 9B shows the window 900 of the GUI. When the user selects a gRNA922 from the top ranked gRNAs 920, the GUI highlights the respectivehybridizing location 942 of the selected gRNA 922 in the selected codingregion 930 of the gene. Furthermore, as shown in FIG. 9C, the window 900also shows details 944 about the selected gRNA 922, including the targetpolynucleotide sequence, the Cas cutsite (cleave site) within the genomeof the species, the selected coding region position, the on-targetvalue, and the off-target value. FIG. 9D shows a window 905 of the GUI.The window 905 displays additional gRNAs from the one or more of gRNAsthat are designed to hybridize the gene of the genome of interest. Theuser can be able to select at least one from the additional gRNAs forfurther analysis and/or purchase.

FIG. 10A-E illustrate examples of a window of the GUI for displayingdetailed information about a gRNA designed to hybridize the gene of thegenome of interest. When the user selects a designed gRNA (e.g., thegRNA 922 in FIG. 9C), the user is directed to a new GUI window 1000, asshown in FIG. 10A. The window 1000 provides a summary 1010 of theperformance the selected gRNA (e.g., the target gene of the gRNA, thelocation of the cut site within the genome, etc.). The window 1000 alsoshows other details 1020 including the selected gRNA sequence, genome,gene, and nuclease selected for the analysis. Furthermore, the window1000 shows a schematic 1030 of the Cas-gRNA complex that drawn tointeract with the target region of the gene of interest. The window 1000also shows examples of off-target sites 1040 of the selected gRNA withaccompanying information including a number of mismatches between theselected gRNA and each of the off-target site, the position of theoff-target site within the genome, and the name of the gene comprisingthe off-target site (a list of additional off-target sites 1045 of theselected gRNA shown in FIG. 10E). When the user chooses differentportions of the schematic 1030, the GUI can inform the user which partof the schematic 1030 is displaying the RNA guide sequence (FIG. 10B,1032), the protospacer adjacent motif (PAM) site in the target site(FIG. 10C, 1034), and the target site sequence (FIG. 10D, 1036).

FIG. 11A-B illustrate examples of windows of the GUI for selecting andpurchasing a subset one of the one or more gRNAs that are designed tohybridize the gene of the genome of interest. FIG. 11A shows the window900 of the GUI. The user can select a subset of gRNAs 1110 from the topranked gRNAs 920 and proceed 1120 to purchase synthesized molecules ofthe subset of gRNAs 1110. FIG. 11B shows the window 1100 that isdisplayed to the user once the user proceeds 1120 to purchase thesynthesized molecules of the subset of gRNAs 1110. The window 1100displays a summary 1130 of the selected gRNAs (e.g., the number ofselected gRNAs and their intended target gene and genome). The window1100 also requests the user to choose between modified gRNA 1140 orunmodified gRNA 1145 for synthesis. In addition, the window 1100displays a final summary 1150 of the gRNAs that are selected forsynthesis. The user can proceed for payment of the purchase.

FIG. 12A-B illustrate examples of a window of a GUI for selecting agenome of a species of interest and inputting a previously generatedgRNA sequence to request validation of the guide performance. The gRNAcan have been previously designed to direct a CRISPR/Cas system for geneediting. FIG. 12A shows the window of the GUI 1200 for gRNA validation.The GUI allows the user to type in the name or the identifier number ofthe genome 1210 and the sequence of the previously determined gRNA 1220.In this GUI, the nuclease of use 1230 is pre-selected to beStreptococcus pyogenes Cas9 (SpCas9). In some cases, the user can selecta Cas enzyme of interest. As shown in FIG. 12B, Once the genome 1215,gRNA sequence 1225, and nuclease 1230 are determined, the user canproceed 1240 to validate the gRNA sequence.

FIG. 13A-B illustrate examples of a window of the GUI for displayingdetailed information about validation of a gRNA that is designed tohybridize the gene of the genome of interest. When the user requests avalidation of a predetermined gRNA (e.g., the gRNA sequence 1225 in FIG.12B), the user is directed to a new GUI window 1300, as shown in FIG.13A. The window 1300 provides a summary 1310 of the performance thepredetermined gRNA (e.g., the predicted target gene of the gRNA, thelocation of the cut site within the genome, etc.). The window 1300 alsoshows other details 1320 including the predetermined gRNA sequence,genome, and nuclease selected for the analysis. Furthermore, the window1300 shows a schematic 1330 of the Cas-gRNA complex that drawn tointeract with the target region of the predicted target gene. The window1300 also shows examples of off-target sites 1340 of the predeterminedgRNA with accompanying information including a number of mismatchesbetween the predetermined gRNA and each of the off-target site, theposition of the off-target site within the genome, and the name of thegene comprising the off-target site, as shown in FIG. 13B.

System

The other different aspect of the present disclosure provides a systemcomprising: (1) an interface that provides a user with access to morethan 10,000 reference genomes; (2) a software for selection of one ormore guide RNAs (gRNAs) for a gene in any one of the more than 50,000reference genomes; and (3) an output system that displays selected guideRNAs.

The system can utilize the aforementioned computer system comprising thecomputer readable medium for performing the method for designing one ormore gRNAs for hybridizing to a genomic region of interest.

The system can comprise from 20,000 to 120,000 reference genomes. Thesystem can comprise at least 20,000, 20,000, 30,000, 40,000, 50,000,60,000, 70,000, 80,000, 90,000, 100,000, 110,000, 120,000, or morereference genomes. The system can comprise at most 120,000, 110,000,100,000, 90,000, 80,000, 70,000, 60,000, 50,000, 40,000, 30,000, 20,000,or less reference genomes.

The system can comprise a machine (e.g., a synthesizer) that synthesizespolynucleotides. The software can be in communication with the machine.Alternatively or in addition to, the system can be in communication withan external machine that synthesizes polynucleotides. In some cases, thesystem can further comprise a script that activates and initiatessynthesis of the gRNAs. The synthesis of the gRNAs can be based on theuser's selection of one or more gRNAs.

The present disclosure further provides a method for designing a guideRNA (gRNA). The method for designing the gRNA can comprise identifying aprimary transcript of a gene. The method for designing the gRNA cancomprise identifying a common exon between the primary transcript and aplurality of alternative transcripts. The method for designing the gRNAcan comprise identifying a nuclease target site within the common exon.The method for designing the gRNA can comprise calculating a number ofoff-target binding sites for the nuclease target site against areference genome sequence, thereby yielding a calculated number ofnuclease off-target binding sites. The method for designing the gRNA cancomprise calculating an on-target efficiency score, thereby yielding acalculated on-target efficiency score. The method for designing the gRNAcan comprise outputting at least one gRNA sequence wherein the gRNAcomprises a sequence for which the calculated on-target efficiency isabove a threshold and the calculated number of nuclease off-targetbinding sites is zero.

In some cases, the method for designing the gRNA can comprise directinga synthesis of a nucleic acid that has partial complementarity to thetarget site. The partial complementarity between the gRNA and the targetsite can comprise a mismatch of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or morethan 10 nucleotides.

The present disclosure further provides a system for processing abiopolymer synthesis request from a user over a network. The system cancomprise a communications interface that is configured to communicatewith a digital computer of the user over the network. The system cancomprise a reference genome database that stores one or more referencegenomes. The system can comprise a computer comprising one or morecomputer processors operatively coupled to the communications interfaceand the database. The one or more computer processors can beindividually or collectively programmed to: (a) receive from thecommunications interface over the network, the biopolymer synthesisrequest from the digital computer of the user, which biopolymersynthesis request comprises target genomic information; (b) process thetarget genomic information against the one or more reference genomesfrom the database to identify a target sequence corresponding to thetarget genomic information; (c) execute an algorithm to generate a firstset of guide ribonucleic acid (gRNA) sequences that are at leastpartially complementary to the target sequence, and calculate anoff-target complementarity score for each of the gRNA sequences in thefirst set of gRNA sequences; (d) output a second set of gRNA sequencesfor display on a graphical user interface of the digital computer of theuser, where each of the second set of gRNA sequences has a calculatedoff-target complementarity score below a threshold; and (e) receive fromthe digital computer of the user a selection of a given gRNA sequencefrom the second set of gRNA sequences.

In some cases, the one or more computer processors can be individuallyor collectively programmed to direct the given gRNA sequence in a queuefor synthesizing the gRNA sequence. In some cases, at least one genomein the reference genome database can be a personalized genome of anindividual. In some cases, at least one genome in the reference genomedatabase can a set of personalized genomes of a population afflictedwith a condition. In some cases, the reference genome can be a Homosapiens reference genome.

In some cases, the one or more computer processors can be individuallyor collectively programmed to output a predicted genomic sequence. Thepredicted genomic sequence can represent a predicted output of editingthe target genomic information with one or more gRNA's from the secondset of gRNA sequences. The predicted genomic sequence can comprise agenomic deletion. The predicted genomic sequence comprises a genomicinsertion.

In some cases, calculating the off-target complementarity scorecomprises calculating an Azimuth score. In some cases, the second set ofgRNA sequences can display at least two gRNAs above a certain threshold.

In some cases, the reference genome database can comprise at least 50thousand reference genomes. In some cases, the reference genome databasecan comprise at least 120 thousand reference genomes.

The present disclosure further provides a method for processing abiopolymer synthesis request from a user over a network. The method cancomprise: (a) receiving the biopolymer synthesis request from a digitalcomputer of the user over the network, which biopolymer synthesisrequest comprises target genomic information; (b) processing the targetgenomic information against one or more reference genomes from areference genome database to identify a target sequence corresponding tothe target genomic information; (c) using one or more computerprocessors to execute an algorithm to (i) generate a first set of guideribonucleic acid (gRNA) sequences that are at least partiallycomplementary to the target sequence, and (ii) calculate an off-targetcomplementarity score for each of the gRNA sequences in the first set ofgRNA sequences for each of the gRNA sequences; (d) outputting a secondset of gRNA sequences for display on a graphical user interface of thedigital computer of the user, where each of the second set of gRNAsequences has a calculated off-target complementarity score below athreshold; and (e) receiving from the digital computer of the user arequest for a synthesis of a given gRNA sequence from the second set ofgRNA sequences.

In some cases, a computer program (e.g., a computer readable medium) canbe configured for instructing a computer to perform the method ofprocessing a biopolymer synthesis request from a user over a network.

In some cases, one or more computer processors receiving the request forthe synthesis can be individually or collectively programmed to directthe synthesis of the given gRNA sequence from the second set of gRNAsequences in a synthesizer. In some cases, at least one genome in thereference genome database can be a personalized genome of an individual.In some cases, at least two genomes in the reference genome database canbe personalized genomes of a population afflicted with a condition. Insome cases, the reference genome can be a Homo sapiens reference genome.

In some cases, the method can further comprise outputting a predictedgenomic sequence. The predicted genomic sequence can represent apredicted output of editing the target genomic information with one ormore gRNAs from the second set of gRNA sequences. In some cases, thepredicted genomic sequence can comprise a genomic deletion. In somecases, the predicted genomic sequence can comprise a genomic insertion.In some cases, the calculating can calculate an Azimuth score. In somecases, the second set of gRNA sequences can display at least two gRNAsabove a certain threshold. In some cases, the reference genome databasecan comprise at least 50 thousand reference genomes. In some cases, thereference genome database can comprise at least 120 thousand referencegenomes.

The present disclosure further provides a non-transitorycomputer-readable medium comprising machine executable code that, uponexecution by one or more computer processors, implements a method forprocessing a biopolymer synthesis request from a user over a network.The method can comprise receiving the biopolymer synthesis request froma digital computer of the user over the network, which biopolymersynthesis request comprises target genomic information. The method cancomprise processing the target genomic information against one or morereference genomes from a reference genome database to identify a targetsequence corresponding to the target genomic information. The method cancomprise executing an algorithm to generate a first set of guideribonucleic acid (gRNA) sequences that are at least partiallycomplementary to the target sequence, and calculate an off-targetcomplementarity score for each of the gRNA sequences in the first set ofgRNA sequences. The method can comprise outputting a second set of gRNAsequences for display on a graphical user interface of the digitalcomputer of the user, where each of the second set of gRNA sequences hasa calculated off-target complementarity score below a threshold. Themethod can comprise receiving from the digital computer of the user aselection of a given gRNA sequence from the second set of gRNA sequences

Computer Systems

The present disclosure provides computer systems that are programmed toimplement methods of the disclosure. Computer systems of the presentdisclosure can be used to design one or more guide RNAs for hybridizingto a genomic region of interest. The genomic region of interest can be agene of a genome of a species. Information from any of the computersystems described herein can provide a report to a remote computer.

FIG. 14 shows a computer system 1401 that is programmed or otherwiseconfigured to communicate with and regulate various aspects of acomputer system of the present disclosure.

The computer system 1401 can regulate various aspects of the presentdisclosure, such as, for example, designing one or more guide RNAs forhybridizing to a gene of a genome of a species, or calculating anoff-target value by enumerating a number of mismatches to potentialguide RNA hybridizing sites in the genome of interest. The computersystem 1401 can be an electronic device of a user or a computer systemthat is remotely located with respect to the electronic device. Theelectronic device can be a mobile electronic device.

The computer system 1401 includes a central processing unit (CPU, also“processor” and “computer processor” herein) 1405, which can be a singlecore or multi core processor, or a plurality of processors for parallelprocessing. The computer system 1401 also includes memory or memorylocation 1410 (e.g., random-access memory, read-only memory, flashmemory), electronic storage unit 1415 (e.g., hard disk), communicationinterface 1420 (e.g., network adapter) for communicating with one ormore other systems, and peripheral devices 1425, such as cache, othermemory, data storage and/or electronic display adapters. The memory1410, storage unit 1415, interface 1420 and peripheral devices 1425 arein communication with the CPU 1405 through a communication bus (solidlines), such as a motherboard. The storage unit 1415 can be a datastorage unit (or data repository) for storing data. The computer system1401 can be operatively coupled to a computer network (“network”) 1430with the aid of the communication interface 1420. The network 1430 canbe the Internet, an internet and/or extranet, or an intranet and/orextranet that is in communication with the Internet. The network 1430 insome cases is a telecommunication and/or data network. The network 1430can include one or more computer servers, which can enable distributedcomputing, such as cloud computing. The network 1430, in some cases withthe aid of the computer system 1401, can implement a peer-to-peernetwork, which can enable devices coupled to the computer system 1401 tobehave as a client or a server.

The CPU 1405 can execute a sequence of machine-readable instructions,which can be embodied in a program or software. The instructions can bestored in a memory location, such as the memory 1410. The instructionscan be directed to the CPU 1405, which can subsequently program orotherwise configure the CPU 1405 to implement methods of the presentdisclosure. Examples of operations performed by the CPU 1405 can includefetch, decode, execute, and writeback.

The CPU 1405 can be part of a circuit, such as an integrated circuit.One or more other components of the system 1401 can be included in thecircuit. In some cases, the circuit is an application specificintegrated circuit (ASIC).

The storage unit 1415 can store files, such as drivers, libraries andsaved programs. The storage unit 1415 can store user data, e.g., userpreferences and user programs. The computer system 1401 in some casescan include one or more additional data storage units that are externalto the computer system 1401, such as located on a remote server that isin communication with the computer system 1401 through an intranet orthe Internet.

The computer system 1401 can communicate with one or more remotecomputer systems through the network 1430. For instance, the computersystem 1401 can communicate with a remote computer system of a user.Examples of remote computer systems include personal computers (e.g.,portable PC), slate or tablet PC's (e.g., Apple® iPad, Samsung® GalaxyTab), telephones, Smart phones (e.g., Apple® iPhone, Android-enableddevice, Blackberry®), or personal digital assistants. The user canaccess the computer system 1401 via the network 1430.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system 1401, such as, for example, on thememory 1410 or electronic storage unit 1415. The machine executable ormachine readable code can be provided in the form of software. Duringuse, the code can be executed by the processor 1405. In some cases, thecode can be retrieved from the storage unit 1415 and stored on thememory 1410 for ready access by the processor 1405. In some situations,the electronic storage unit 1415 can be precluded, andmachine-executable instructions are stored on memory 1410.

The code can be pre-compiled and configured for use with a machinehaving a processer adapted to execute the code, or can be compiledduring runtime. The code can be supplied in a programming language thatcan be selected to enable the code to execute in a pre-compiled oras-compiled fashion.

Aspects of the systems and methods provided herein, such as the computersystem 1401, can be embodied in programming. Various aspects of thetechnology can be thought of as “products” or “articles of manufacture”typically in the form of machine (or processor) executable code and/orassociated data that is carried on or embodied in a type of machinereadable medium. Machine-executable code can be stored on an electronicstorage unit, such as memory (e.g., read-only memory, random-accessmemory, flash memory) or a hard disk. “Storage” type media can includeany or all of the tangible memory of the computers, processors or thelike, or associated modules thereof, such as various semiconductormemories, tape drives, disk drives and the like, which can providenon-transitory storage at any time for the software programming. All orportions of the software can at times be communicated through theInternet or various other telecommunication networks. Suchcommunications, for example, can enable loading of the software from onecomputer or processor into another, for example, from a managementserver or host computer into the computer platform of an applicationserver. Thus, another type of media that can bear the software elementsincludes optical, electrical and electromagnetic waves, such as usedacross physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks or the like, also can be considered as media bearing the software.As used herein, unless restricted to non-transitory, tangible “storage”media, terms such as computer or machine “readable medium” refer to anymedium that participates in providing instructions to a processor forexecution.

Hence, a machine readable medium, such as computer-executable code, cantake many forms, including but not limited to, a tangible storagemedium, a carrier wave medium or physical transmission medium.Non-volatile storage media include, for example, optical or magneticdisks, such as any of the storage devices in any computer(s) or thelike, such as can be used to implement the databases, etc. shown in thedrawings. Volatile storage media include dynamic memory, such as mainmemory of such a computer platform. Tangible transmission media includecoaxial cables; copper wire and fiber optics, including the wires thatcomprise a bus within a computer system. Carrier-wave transmission mediacan take the form of electric or electromagnetic signals, or acoustic orlight waves such as those generated during radio frequency (RF) andinfrared (IR) data communications. Common forms of computer-readablemedia therefore include for example: a floppy disk, a flexible disk,hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a ROM, a PROM andEPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer can readprogramming code and/or data. Many of these forms of computer readablemedia can be involved in carrying one or more sequences of one or moreinstructions to a processor for execution.

The computer system 1401 can include or be in communication with anelectronic display 1435 that comprises a user interface (UI) 1440 forproviding the use, for example, the ability to select a species ofinterest and gene of interest from the species of interest. Examples ofUI's include, without limitation, a graphical user interface (GUI) andweb-based user interface.

Methods and systems of the present disclosure can be implemented by wayof one or more algorithms. An algorithm can be implemented by way ofsoftware upon execution by the central processing unit 1405. Thealgorithm can, for example, design one or more gRNAs for hybridizing toa genomic region of interest of a species, and activate and initiatesynthesis of at least one of the one or more gRNAs.

EXAMPLES Example 1: Use of Multiple Guide RNAs Achieves Higher EditingEfficiency than Use of a Single Guide RNAs

A total of 228 guide RNAs were designed to hybridize 76 genes, withthree guide RNAs designed per gene. Each set of three guide RNAs wasdesigned such that the inter-guide spacing was at least 30 bp. GuideRNAs were introduced into HEK293 and MCF7 cells seeded at 35,000 cellsper well on a 96 well plate. Guide RNAs used in single guide editingwere transfected at 4.5 μmol, while guide RNAs for multi-guide RNA usewere transfected at 2.25 μmol each. All guide RNAs were transfected asribonucleoproteins (RNPs) through nucleofection. Prior to transfection,0.5 μmol of Cas9 was complexed with the RNPs. At 2 dayspost-transfection the resulting genotype of the cells was interrogatedvia Sanger sequencing, and the overall editing efficiency was analyzedusing Inference of CRISPR Edits (ICE). The percent editing efficiencywas used to indicate a percentage of cells comprising a non-wild typegenotype at the location at which the gRNAs were designed to edit.Editing efficiency was assessed for the use of a single gRNA per gene aswell as a set of three gRNA per gene (FIG. 15). Boxplots behind thedatapoints indicate the median, 25/75^(th) percentiles and 5/95^(th)percentiles. These results indicated that use of three gRNAs with thespecified inter-guide spacing achieved a higher editing efficiency thanthe use of a single gRNA that hybridizes to a target gene (p<1E-15,Mann-Whitney U test).

Editing outcomes using multi-guide sgRNAs were further analyzed for theeffect of guide spacing. 537 gRNAs were designed to hybridize 179 genes,with three gRNAs designed per gene. These gRNAs were designed withinter-guide spacing between −20 bp (i.e. completely overlapping) to 80bp. Guide RNAs for multi-guide were transfected at 2.25 μmol each. AllgRNAs were transfected as ribonucleoproteins (RNPs) throughnucleofection. Prior to transfection, 0.5 μmol Cas9 was complexed withthe RNPs. At 2 days post-transfection the resulting genotype of thecells was interrogated via Sanger sequencing, and the overall editingefficiency was analyzed. As the inter-guide spacing (i.e. theend-to-start distance between guides) increased above 30 base pairs(bp), the overall editing efficiency improved such that efficienciesless than 75% were not observed (FIG. 16).

Example 2: Combinations of Multiple Guide RNA Kits

Nine sgRNAs were designed, three sgRNAs to target each of three targetregions within a human genome: two genes, protein argininemethyltransferase 5 (PRMTS) and methylthioadenosine phosphorylase(MTAP), as well as a site within adeno-associated virus integration site1 (AAVS1). In each set of sgRNAs, the inter-guide spacing was at least30 bp. Editing efficiency for each gene in each pairwise combination wasdetermined via Sanger sequencing (FIG. 17).

5000 Hep3B cells were seeded per well in a 96-well plate. UsingNucleofector™ technology (Lonza), these cells were transfected withmultiple RNPs targeting the pairs, and cell titers were assayed at 24,48, and 72 post transfection. These results indicate that one can createedits at multiple genomic loci simultaneously in a single transfection.

Example 3: Arrayed Library Screening

35,000 U2OS cells were seeded per well in 92 wells of a 96 well plate.Each of the 92 wells further contained a set of 2 or 3 sgRNAs targetinga gene with inter-guide spacing of at least 30 bp, for a total of 92different genes targeted by the screening assay, and a Cas9endonuclease. Cells were transfected using Nucleofector™ technology(Lonza). Cell viability was subsequently assessed using a CellTiter-Glo®Luminescent Cell Viability Assay at 6 days post-transfection (FIG. 18A).Additionally, these cells were genotyped with Sanger sequencing at 2days post-transfection, followed by analysis using Inference of CRISPREdits (ICE) to determine editing efficiency (FIG. 18B). The ability toassess the resulting genotype of the same population of cells can be anadvantage of arrayed screening approaches over pooled screeningapproaches.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. It is not intendedthat the invention be limited by the specific examples provided withinthe specification. While the invention has been described with referenceto the aforementioned specification, the descriptions and illustrationsof the embodiments herein are not meant to be construed in a limitingsense. Numerous variations, changes, and substitutions will now occur tothose skilled in the art without departing from the invention.Furthermore, it shall be understood that all aspects of the inventionare not limited to the specific depictions, configurations or relativeproportions set forth herein which depend upon a variety of conditionsand variables. It should be understood that various alternatives to theembodiments of the invention described herein can be employed inpracticing the invention. It is therefore contemplated that theinvention shall also cover any such alternatives, modifications,variations or equivalents. It is intended that the following claimsdefine the scope of the invention and that methods and structures withinthe scope of these claims and their equivalents be covered thereby.

1. A system for modifying a gene within a genomic region of interest ina cell, comprising: (a) a set of guide RNAs (gRNAs) comprising (i) afirst gRNA configured to hybridize to a genomic region of interestwithin a gene in a cell, (ii) a second gRNA configured to hybridize tothe genomic region of interest within the gene in the cell, and (iii) athird guide RNA configured to hybridize to the genomic region ofinterest within the gene in the cell, wherein the first gRNA, the secondgRNA, and the third gRNA are different, and wherein the first gRNA isconfigured to hybridize to a binding site that is at least 30 base pairsapart from a binding site hybridizable to the second gRNA and the secondgRNA is configured to hybridize to a binding site that is at least 30base pairs apart from a binding site hybridizable to the third gRNA; and(b) a nuclease, wherein the nuclease generates an edit that modifies thegene, wherein the cell is contacted with the set of gRNAs and thenuclease, wherein the first gRNA forms a complex with the nuclease toform a first complex, the second gRNA forms a complex with the nucleaseto form a second complex, and the third gRNA forms a complex with thenuclease to form a third complex, and wherein each of the first complex,the second complex, and the third complex is configured to generate adouble-strand break in the genomic region of interest.
 2. The system ofclaim 1, further comprising a donor polynucleotide comprising a desiredgene edit sequence, wherein the cell is contacted with the donorpolynucleotide and repair of the double-stranded break in the genomicregion of interest by a DNA repair process leads to integration of thedesired gene edit sequence.
 3. The system of claim 2, wherein thedesired gene edit sequence comprises a point mutation, an allele, a tag,or an exogenous exon relative to a wild-type genotype of the cell. 4.The system of claim 1, further comprising another set of gRNAsconfigured to hybridize to another genomic region of interest, whereinthe genomic region of interest and the another genomic region ofinterest are different, and wherein the cell is contacted with theanother set of gRNAs.
 5. The system of claim 1, further comprisinganother set of gRNAs configured to hybridize to another genomic regionof interest, wherein an another cell is contacted with the another setof gRNAs, and wherein the cell and the another cell are different, theset of gRNAs and the another set of gRNAs are different, and the genomicregion of interest and the another genomic region of interest aredifferent.
 6. The system of claim 1, wherein the contacting comprisestransfecting the cell with the set of gRNAs as ribonucleoproteincomplexes.
 7. The system of claim 1, wherein the first gRNA, the secondgRNA, and the third gRNA comprise a 5′ end modification and a 3′ endmodification, wherein the 5′ end modification comprises aphosphorothioate internucleotide linkage and a 2′-O-methyl sugarmodification and the 3′ end modification comprises a phosphorothioateinternucleotide linkage and a 2′-O-methyl sugar modification.
 8. Thesystem of claim 1, wherein the nuclease comprises a Cas9 nuclease. 9.The system of claim 1, wherein the edit is a deletion.
 10. The system ofclaim 9, wherein the deletion is at least 100 base pairs.
 11. The systemof claim 1, wherein the edit is an indel.
 12. The system of claim 1,wherein the edit is a knock-out.
 13. The system of claim 12, wherein theknock-out eliminates a function of the gene.
 14. The system of claim 1,wherein the edit improves a function of the gene.
 15. The system ofclaim 1, wherein the first gRNA is configured to hybridize to a bindingsite that is 30-150 base pairs apart from a binding site hybridizable tothe second gRNA and the second gRNA is configured to hybridize to abinding site that is 30-150 base pairs apart from a binding sitehybridizable to the third gRNA.
 16. The system of claim 1, wherein thefirst gRNA, the second gRNA, and the third gRNA are configured tohybridize to the gene.
 17. The system of claim 16, wherein the firstgRNA, the second gRNA, and the third gRNA are configured to hybridize toan exon in the gene.
 18. The system of claim 17, wherein the exon is afirst exon of the gene.
 19. The system of claim 1, wherein the firstgRNA, the second gRNA, or the third gRNA is configured to hybridize to atrans-regulatory unit.
 20. The system of claim 1, wherein the firstgRNA, the second gRNA, or the third gRNA is configured to hybridize to acis-regulatory unit.